{"id":3592,"url":"https://github.com/engyasin/awesome-mechatronics","name":"awesome-mechatronics","description":"A list of awesome mechatronics books, courses and software.","projects_count":293,"last_synced_at":"2026-09-14T13:00:19.589Z","repository":{"id":114548741,"uuid":"217821682","full_name":"engyasin/awesome-mechatronics","owner":"engyasin","description":"A list of awesome mechatronics books, courses and software.","archived":false,"fork":false,"pushed_at":"2026-08-08T12:45:23.000Z","size":91,"stargazers_count":204,"open_issues_count":1,"forks_count":24,"subscribers_count":5,"default_branch":"master","last_synced_at":"2026-08-25T18:10:48.638Z","etag":null,"topics":["awesome","awesome-list","electrical-engineering","industrial-automation","iot","mechanical-engineering","mechatronic-systems","mechatronics","mechatronics-engineering","python","robotics","ros2"],"latest_commit_sha":null,"homepage":"https://www.rlbyexample.net/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/engyasin.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-10-27T07:49:32.000Z","updated_at":"2026-08-22T19:33:34.000Z","dependencies_parsed_at":null,"dependency_job_id":"a501da4e-1a80-43ae-ae04-1a439e6c0d3b","html_url":"https://github.com/engyasin/awesome-mechatronics","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/engyasin/awesome-mechatronics","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/engyasin%2Fawesome-mechatronics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/engyasin%2Fawesome-mechatronics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/engyasin%2Fawesome-mechatronics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/engyasin%2Fawesome-mechatronics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/engyasin","download_url":"https://codeload.github.com/engyasin/awesome-mechatronics/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/engyasin%2Fawesome-mechatronics/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":341189360,"owners_count":37315793,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-22T15:14:58.755Z","status":"online","status_checked_at":"2026-09-14T02:00:06.290Z","response_time":176,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-06T00:28:34.464Z","updated_at":"2026-09-14T13:00:19.589Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["14. Related Awesome Lists","Related awesome lists ##","Software and Libraries ##","8. Agentic AI in Automation and Robotics","Licence","3. The Mechanical Side, Seen From Mechatronics","6. The Robotics Software Stack","4. Electronics, Embedded \u0026 Edge","Programming Langauges ##","5. Industrial Automation, Industry 4.0 / 5.0","2. Foundations: Books \u0026 Courses","10. Hardware You Can Actually Buy or Build","11. Classic Mechatronic Systems","Books ##","13. Journals, Conferences, Communities","Journals and Conferences ##","Mechatronic Systems ##","7. Learning-Based Control: The New Core","1. Start Here: Learning Paths","0. What Mechatronics Means Now","9. Hands-On Projects"],"sub_categories":["8.4 Safety, assurance and governance","8.2 Protocols and building blocks","3.4 Design, simulation and manufacturing tools","Simulation — this changed completely in 2025–2026","Perception","Microcontrollers \u0026 compute","Electronics design","Firmware \u0026 RTOS","Tooling \u0026 visualisation","ROS 2 — current state (as of mid-2026)","Connectivity, information models and digital twins","Machine learning for engineers 📖","Edge AI / TinyML","Control \u0026 dynamics (free where possible) 📖","Courses worth your time 🎓","Simulation \u0026 commissioning","3.2 Compliance, contact and impedance","Kinematics, dynamics \u0026 optimisation libraries","Core mechatronics textbooks 📖","8.3 Agents in robotics (as opposed to plant floors)","7.4 Vision-Language-Action models (VLAs)","3.1 Actuation \u0026 transmission","Path B — Undergraduate / career-switcher (6–18 months)","Path C — Graduate / research 🔬","Path A — Beginner, hands-on first (0–6 months) ⭐","Robotics 📖","7.3 Flow matching \u0026 real-time chunking","7.7 Datasets \u0026 benchmarks","7.8 Hands-on: train your own policy 🧪","7.6 Reinforcement learning on real hardware","7.1 Imitation learning \u0026 action chunking","7.5 World models 🔬","7.2 Diffusion policies","Read the definition debate yourself 📄","3.3 Mechanical intelligence \u0026 morphological computation 🔬","Controllers \u0026 languages"],"readme":"# Awesome Mechatronics [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) [![License: CC0-1.0](https://img.shields.io/badge/License-CC0_1.0-lightgrey.svg)](https://creativecommons.org/publicdomain/zero/1.0/)\n\n\u003c!-- Alternative header image — the classic mechatronics Venn diagram.\n     Re-enable by uncommenting the block below.\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/mechatronics-venn.svg\" width=\"300\" alt=\"The classic mechatronics Venn diagram: mechanical, electrical and computer engineering overlapping at control\"\u003e\n\u003c/p\u003e\n--\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cem\u003eBooks, courses, tools, papers and hardware for mechatronic engineering —\u003cbr\u003efrom the classical V-model to vision-language-action models.\u003c/em\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"#0-what-mechatronics-means-now\"\u003eDefinitions\u003c/a\u003e ·\n  \u003ca href=\"#1-start-here-learning-paths\"\u003eLearning paths\u003c/a\u003e ·\n  \u003ca href=\"#7-learning-based-control-the-new-core\"\u003eVLAs \u0026amp; diffusion policies\u003c/a\u003e ·\n  \u003ca href=\"#8-agentic-ai-in-automation-and-robotics\"\u003eAgentic AI\u003c/a\u003e ·\n  \u003ca href=\"#9-hands-on-projects\"\u003eProjects\u003c/a\u003e ·\n  \u003ca href=\"#12-trends-radar-2026\"\u003eTrends radar\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/mechatronics-stack-2026.svg\" width=\"900\" alt=\"The mechatronic stack in 2026: seven layers from mechanism and structure up to supervision and orchestration, with safety, simulation and data as cross-cutting concerns\"\u003e\n\u003c/p\u003e\n\n---\n\n\u003e **Mechatronics** is the synergistic integration of mechanical engineering, electronics, control theory and computing in the design of products and processes. The 1969 Yaskawa coinage described *machines with electronics inside*. The discipline has since absorbed cyber-physical systems, digital twins, and — since roughly 2023 — learned, language-conditioned policies that replace hand-written control laws. See [§0](#0-what-mechatronics-means-now) for how the definition has moved.\n\n**What's in here.** This list keeps the classical mechatronics canon (it still matters — nothing about foundation models repeals the Nyquist criterion) and adds the parts of the field that appeared in the last five years: learned visuomotor control, VLAs, diffusion and flow-matching policies, GPU physics, agentic industrial software, and the mechanical-design ideas that came back into fashion because learning made compliant hardware tractable.\n\n**Legend** — 📖 book · 📄 paper · 🎓 course · 🔧 tool · 📝 write-up · 🧪 hands-on · 🆓 free / open source · 💵 paid · ⭐ start here if you're new · 🔬 research-level\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eTable of contents\u003c/strong\u003e\u003c/summary\u003e\n\n- [0. What Mechatronics Means Now](#0-what-mechatronics-means-now)\n- [1. Start Here: Learning Paths](#1-start-here-learning-paths)\n- [2. Foundations: Books \u0026 Courses](#2-foundations-books--courses)\n- [3. The Mechanical Side, Seen From Mechatronics](#3-the-mechanical-side-seen-from-mechatronics)\n- [4. Electronics, Embedded \u0026 Edge](#4-electronics-embedded--edge)\n- [5. Industrial Automation, Industry 4.0 / 5.0](#5-industrial-automation-industry-40--50)\n- [6. The Robotics Software Stack](#6-the-robotics-software-stack)\n- [7. Learning-Based Control: The New Core](#7-learning-based-control-the-new-core)\n- [8. Agentic AI in Automation and Robotics](#8-agentic-ai-in-automation-and-robotics)\n- [9. Hands-On Projects](#9-hands-on-projects)\n- [10. Hardware You Can Actually Buy or Build](#10-hardware-you-can-actually-buy-or-build)\n- [11. Classic Mechatronic Systems](#11-classic-mechatronic-systems)\n- [12. Trends Radar 2026](#12-trends-radar-2026)\n- [13. Journals, Conferences, Communities](#13-journals-conferences-communities)\n- [14. Related Awesome Lists](#14-related-awesome-lists)\n- [Contributing](#contributing)\n- [Licence](#licence)\n\n\u003c/details\u003e\n\n---\n\n## 0. What Mechatronics Means Now\n\n### The definition has moved four times\n\nMechatronics has been redefined roughly once per industrial revolution, and the current redefinition is the sharpest since the 1990s.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/mechatronics-definition-evolution.svg\" width=\"900\" alt=\"Timeline of the mechatronics definition: 1969 Yaskawa coinage, 1980s-90s synergistic integration, 2011 cyber-physical systems, 2020 human cyber-physical systems, 2023-26 embodied intelligence\"\u003e\n\u003c/p\u003e\n\n\n**1. Mechanics + electronics → synergistic integration.** The 1980s–90s reframing was that mechatronics is not a sum of parts bolted together but a *co-design* discipline: the mechanism, the sensor choice and the control law are decided together, from the first sketch. This is where the V-model and concurrent engineering come from, and it remains the core professional skill.\n\n**2. Mechatronics → Cyber-Physical Systems.** With Industry 4.0 (2011), production systems became networked CPS built on digital twins and standardised information models. Mechatronics stopped being about *one* machine and started being about fleets of machines with an information layer. The Asset Administration Shell (IEC 63278-1:2023) is the concrete artefact of this shift.\n\n**3. CPS → Human-CPS (Industry 5.0).** From around 2020, the framing added human-centricity, resilience and sustainability, giving rise to *Human* Cyber-Physical Systems and cobots. A useful consequence: three industrial paradigms now coexist on the same shop floor, and a mechatronics engineer is expected to work across all three.\n\n**4. Everything → Physical AI / Embodied AI.** The 2024–2026 shift is the largest. Perception-to-action neural policies increasingly *replace* the hand-designed controller, not just augment it. Terminology is genuinely contested here:\n\n| Term | Rough meaning | Who pushes it |\n|---|---|---|\n| **Embodied AI** | AI that perceives, decides and acts through a body (physical *or* simulated). The older, academic term. | Academia; ITU-T Rec. **F.748.66** (Dec 2025) gives it a formal framework |\n| **Physical AI** | The broader commercial umbrella: models + simulation + compute sold as one stack for real-world machines | NVIDIA, BCG, investors |\n| **Mechatronics** | The engineering discipline that actually builds the body the AI acts through | Everyone, once the demo has to ship |\n\nThe honest reading: *Physical AI is a market category; mechatronics is the engineering discipline it depends on.* Nothing in a VLA solves backlash, thermal derating, or the fact that a harmonic drive has a torque ripple signature. What has changed is where the difficulty sits — less in writing the controller, more in **hardware that is learnable**: backdrivable, well-instrumented, repeatable, and cheap enough to collect thousands of demonstrations on.\n\n### A working definition for 2026\n\n\u003e Mechatronics is the design of systems whose behaviour emerges from the co-design of mechanism, actuation, sensing, computation and *learned or programmed policy* — where the policy may now be a neural network trained on data rather than a control law derived from a model.\n\n### Read the definition debate yourself 📄\n\n- [The Evolution of Mechatronics Engineering and Its Relationship with Industry 3.0, 4.0, and 5.0](https://doi.org/10.3390/technologies14020081) — *Technologies*, 2026. 🆓 The single best recent paper on this question; proposes a generational classification of machines. **Start here.** ⭐\n- [Mechatronics in Industry 4.0 and 5.0: advancing synergy, innovations, sustainability, and challenges](https://www.researchgate.net/publication/390078056) — Maki K. Habib, 2025.\n- [Skills for Physical Artificial Intelligence](https://www.nature.com/articles/s42256-020-0177-2) — Miriyev \u0026 Kovač, *Nature Machine Intelligence*, 2020. 📄 The paper that named the skills gap between materials, mechanics and AI.\n- [Intelligence as Computation](https://arxiv.org/abs/2412.14701) — Oliver Brock, 2024. 🔬 Argues digital, analog, mechanical and morphological computation are one continuum — the philosophical backbone for \"mechanical intelligence.\"\n- [ITU-T F.748.66](https://www.itu.int/rec/T-REC-F.748.66) — *Requirements and framework for embodied artificial intelligence systems*, approved Dec 2025. The first ITU-level attempt to standardise what \"embodied AI system\" means.\n- [Evolution from mechatronics to cyber-physical systems: an educational point of view](https://www.researchgate.net/publication/306300681) — still the clearest statement of the CPS transition for curricula.\n\n---\n\n## 1. Start Here: Learning Paths\n\nMechatronics is wide enough that \"where do I start\" is the most common question. Three paths, depending on where you want to end up.\n\n```mermaid\nflowchart TD\n    A[\"Absolute beginner\u003cbr/\u003eArduino / ESP32 blink → sensors → PWM motor\"] --\u003e B[\"Circuits + C/C++ + Python\"]\n    B --\u003e C[\"Classical control\u003cbr/\u003ePID, Laplace, Bode, state space\"]\n    C --\u003e D{\"Pick a direction\"}\n\n    D --\u003e E[\"\u003cb\u003eIndustrial automation\u003c/b\u003e\u003cbr/\u003ePLC · IEC 61131-3 · HMI/SCADA\u003cbr/\u003eOPC UA · digital twin\"]\n    D --\u003e F[\"\u003cb\u003eRobotics\u003c/b\u003e\u003cbr/\u003eROS 2 · kinematics · SLAM\u003cbr/\u003eMoveIt · Nav2\"]\n    D --\u003e G[\"\u003cb\u003eMachine / product design\u003c/b\u003e\u003cbr/\u003eCAD · FEA · actuator sizing\u003cbr/\u003etolerances · DfM\"]\n\n    E --\u003e H[\"Agentic industrial AI\u003cbr/\u003eMCP · anomaly agents · predictive maintenance\"]\n    F --\u003e I[\"Robot learning\u003cbr/\u003eimitation → ACT → diffusion policy → VLA\"]\n    G --\u003e J[\"Co-design\u003cbr/\u003ecompliant actuators · topology opt · morphological computation\"]\n\n    H --\u003e K[\"\u003cb\u003ePhysical AI engineer\u003c/b\u003e\"]\n    I --\u003e K\n    J --\u003e K\n```\n\n### Path A — Beginner, hands-on first (0–6 months) ⭐\n\nYou want to build something that moves before you learn Laplace transforms. This is a legitimate order.\n\n1. **Get a board and a motor.** Arduino Uno/ESP32 + L298N or a TB6612 + a cheap DC gearmotor with an encoder. Total cost under €40.\n2. 🎓 [Paul McWhorter — Arduino for Beginners](https://www.youtube.com/@paulmcwhorter) 🆓 — the most patient beginner series that exists.\n3. 🎓 [ControlSystemsAcademy / Brian Douglas — Control System Lectures](https://www.youtube.com/@BrianBDouglas) 🆓 — intuition before mathematics. Watch \"PID Control — A brief introduction\" and the root locus series.\n4. 🧪 Build a **closed-loop position controller** for one motor. Tune a PID by hand. This single project teaches sampling, quantisation, saturation, integral windup and derivative noise — the four things that actually bite in practice.\n5. 🎓 [MATLAB Tech Talks — Understanding PID Control](https://www.mathworks.com/videos/series/understanding-pid-control.html) 🆓 (free videos even without a MATLAB licence)\n6. Move to **ESP32 + micro-ROS** or **Raspberry Pi + ROS 2** and you're in the robotics world proper.\n7. 🧪 Then: [LeRobot + an SO-101 arm](#78-hands-on-train-your-own-policy-) — collect 50 demonstrations, train a policy, watch it work. Two hours, and it will reframe everything you thought robot programming was.\n\n### Path B — Undergraduate / career-switcher (6–18 months)\n\n1. **Control**: 🎓 [Steve Brunton — Control Bootcamp](https://www.youtube.com/playlist?list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m) 🆓 + 📖 [Feedback Systems](https://fbswiki.org/) (Åström \u0026 Murray) 🆓\n2. **Robotics**: 📖 [Modern Robotics](http://hades.mech.northwestern.edu/index.php/Modern_Robotics) (Lynch \u0026 Park) 🆓 + the [Coursera specialisation](https://www.coursera.org/specializations/modernrobotics)\n3. **Embedded**: build one project in bare-metal C on an STM32, then one on Zephyr RTOS. The contrast teaches you what an RTOS buys you.\n4. **Industrial**: one PLC project in Structured Text on [OpenPLC](https://autonomylogic.com/) 🆓 or a real S7-1200, plus one OPC UA client.\n5. **Learning**: 📖 [Robot Learning: A Tutorial](https://arxiv.org/abs/2510.12403) 🆓 — the single best on-ramp from classical control to learned policies, with runnable `lerobot` examples.\n\n### Path C — Graduate / research 🔬\n\n1. 📖 [Underactuated Robotics](https://underactuated.mit.edu/) and [Robotic Manipulation](https://manipulation.mit.edu/) — Russ Tedrake, MIT. 🆓 Free, interactive, with Drake notebooks. The best treatment anywhere of *why* contact-rich control is hard.\n2. 📄 [Towards a Unified Understanding of Robot Manipulation: A Comprehensive Survey](https://arxiv.org/abs/2510.10903) — 2025. The map of the whole manipulation literature.\n3. 📄 [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://vla-survey.github.io/) — Kawaharazuka et al., *IEEE Access* 2025. Has a searchable database of every VLA.\n4. Pick a benchmark ([LIBERO](https://libero-project.github.io/), [SimplerEnv](https://github.com/simpler-env/SimplerEnv), [RoboCasa](https://robocasa.ai/)) and reproduce one result before you propose anything.\n\n---\n\n## 2. Foundations: Books \u0026 Courses\n\n### Core mechatronics textbooks 📖\n\nThe classics, kept because they are still the right first books:\n\n- [Mechatronics: Electronic Control Systems in Mechanical and Electrical Engineering](https://www.amazon.com/Mechatronics-Electronic-Mechanical-Electrical-Engineering/dp/1292076682) — W. Bolton, 7th ed. 💵 The standard undergraduate entry point.\n- [Introduction to Mechatronics and Measurement Systems](https://www.amazon.com/Introduction-Mechatronics-Measurement-Systems-Alciatore/dp/1259892344) — Alciatore \u0026 Histand, 5th ed. 💵 Stronger on instrumentation and signal conditioning than Bolton.\n- [The Mechatronics Handbook](http://www.sze.hu/~szenasy/Szenzorok%20%E9s%20aktu%E1torok/Szenzakt%20jegyzetek/Mechatronics_handbook%5B1%5D.pdf) — Robert H. Bishop (ed.). 🆓 first-edition PDF. Reference work, not a textbook.\n- [Mechatronic Systems: Modelling and Simulation with HDLs](https://www.amazon.com/Mechatronic-Systems-Modelling-Simulation-HDLs/dp/0470849797) — Pelz. 💵 Underrated: HDL-based modelling of mixed-domain systems.\n- [Control of Mechatronic Systems](https://www.wiley.com/en-us/Control+of+Mechatronic+Systems-p-9781119505792) — Patrick O. J. Kaltjob, 2021. 💵 One of the few recent books that treats digital control implementation seriously.\n- [Machine Vision and Mechatronics in Practice](https://www.amazon.com/Machine-Vision-Mechatronics-Practice-Billingsley/dp/3662455137) — Billingsley \u0026 Brett. 💵\n- [Automotive Mechatronics](https://www.amazon.com/Automotive-Mechatronics-Electronics-Professional-Information/dp/3658039744) — Bosch Professional Automotive Information. 💵 The reference for automotive networking, ESP/ABS, and drivetrain electronics.\n\n### Control \u0026 dynamics (free where possible) 📖\n\n- [Feedback Systems: An Introduction for Scientists and Engineers](https://fbswiki.org/) — Åström \u0026 Murray, 2nd ed. 🆓 Free PDF. The best free control textbook.\n- [Data-Driven Science and Engineering](https://www.databookuw.com/) — Brunton \u0026 Kutz, 2nd ed. 🆓 Free PDF + code. SVD, DMD, SINDy, MPC, RL, all with runnable examples.\n- [Applied Nonlinear Control](https://www.amazon.com/Applied-Nonlinear-Control-Jean-Jacques-Slotine/dp/0130408905) — Slotine \u0026 Li. 💵 Still the reference for Lyapunov design and sliding mode.\n- [Predictive Control for Linear and Hybrid Systems](https://www.cambridge.org/highereducation/books/predictive-control-for-linear-and-hybrid-systems/EF618BD7AFAF4D04B2044A0FD03D885A) — Borrelli, Bemporad, Morari, Cambridge 2017. 💵\n- [Rigid Body Dynamics Algorithms](https://link.springer.com/book/10.1007/978-1-4899-7560-7) — Roy Featherstone. 💵 If you ever need to know *why* your dynamics library is fast.\n\n### Robotics 📖\n\n- [Modern Robotics: Mechanics, Planning, and Control](http://hades.mech.northwestern.edu/index.php/Modern_Robotics) — Lynch \u0026 Park. 🆓 Free PDF, free videos, free software library, matching Coursera specialisation. ⭐ **The best value in robotics education.**\n- [Robotics: Modelling, Planning and Control](https://link.springer.com/book/10.1007/978-1-84628-642-1) — Siciliano, Sciavicco, Villani, Oriolo. 💵 The European standard text.\n- [Springer Handbook of Robotics](https://link.springer.com/referencework/10.1007/978-3-319-32552-1) — Siciliano \u0026 Khatib (eds.), 2nd ed. 💵 2,300 pages; the field's encyclopedia.\n- [Probabilistic Robotics](https://mitpress.mit.edu/9780262201629/probabilistic-robotics/) — Thrun, Burgard, Fox. 💵 Still the reference for state estimation and SLAM.\n- [Underactuated Robotics](https://underactuated.mit.edu/) — Russ Tedrake, MIT 6.832. 🆓 Free, interactive, updated every year. 🔬\n- [Robotic Manipulation](https://manipulation.mit.edu/) — Russ Tedrake, MIT 6.4210. 🆓 Perception, planning and control for manipulation, with Drake.\n- [Introduction to Autonomous Mobile Robots](https://mitpress.mit.edu/9780262015356/) — Siegwart, Nourbakhsh, Scaramuzza. 💵\n\n### Machine learning for engineers 📖\n\n- [Deep Learning](https://www.deeplearningbook.org/) — Goodfellow, Bengio, Courville. 🆓 Now historical, but the maths chapters age well.\n- [Dive into Deep Learning](https://d2l.ai/) — Zhang et al. 🆓 Interactive, PyTorch/JAX, actually current.\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book.html) — Sutton \u0026 Barto, 2nd ed. 🆓\n- [Spinning Up in Deep RL](https://spinningup.openai.com/) — OpenAI. 🆓 The clearest practical RL introduction; algorithms implemented readably.\n- [Probabilistic Machine Learning](https://probml.github.io/pml-book/) — Kevin Murphy. 🆓 Two volumes, free PDFs.\n- [Computer Vision: Algorithms and Applications](https://szeliski.org/Book/) — Szeliski, 2nd ed. 🆓\n\n### Courses worth your time 🎓\n\n| Course | Who | Cost | Why |\n|---|---|---|---|\n| [Control Bootcamp](https://www.youtube.com/playlist?list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m) | Steve Brunton, UW | 🆓 | Best state-space introduction on YouTube |\n| [Modern Robotics Specialization](https://www.coursera.org/specializations/modernrobotics) | Northwestern | 🆓 audit | Screw theory done properly, with CoppeliaSim labs |\n| [Underactuated Robotics](https://underactuated.mit.edu/) | MIT | 🆓 | Trajectory optimisation, LQR trees, contact 🔬 |\n| [Robotic Manipulation](https://manipulation.mit.edu/) | MIT | 🆓 | The manipulation course, with Drake notebooks 🔬 |\n| [SLAM lectures](https://www.youtube.com/@CyrillStachniss) | Cyrill Stachniss, Bonn | 🆓 | The reference SLAM lecture series |\n| [Sim-to-Real with the SO-101](https://docs.nvidia.com/learning/physical-ai/sim-to-real-so-101/latest/) | NVIDIA | 🆓 | Full pipeline: Isaac Sim → Isaac Lab → GR00T → real arm 🧪 |\n| [LeRobot docs \u0026 tutorials](https://huggingface.co/docs/lerobot) | Hugging Face | 🆓 | Imitation learning and VLAs, hands-on, end to end ⭐ |\n| [Introduction to Robotics CS223A](https://see.stanford.edu/Course/CS223A) | Khatib, Stanford | 🆓 | Classic; operational space control from the source |\n| [Learn 5 PLCs in a Day](https://www.udemy.com/course/nfi-plc-online-leaning/) | Udemy | 💵 | Practical multi-vendor PLC exposure |\n| [From Wire to PLC](https://www.udemy.com/course/from-wire-to-plc-a-to-z-compilation/) | Udemy | 💵 | Panel wiring → ladder → commissioning |\n| [Wearable Robotics — Exoskeletons](https://www.udemy.com/course/wearable-robots-robotic-exoskeleton-lower-limb/) | Udemy | 💵 | Niche but well-made |\n\n---\n\n## 3. The Mechanical Side, Seen From Mechatronics\n\nThe most common failure mode in modern mechatronics projects is a good policy on bad hardware. This section is the mechanical engineering that a mechatronic engineer specifically needs — not general machine design, but the subset where mechanics and control interact.\n\n### The central idea: your mechanism is part of your controller\n\n```mermaid\nflowchart LR\n    subgraph Physical[\"Mechanical domain\"]\n        M[\"Mechanism\u003cbr/\u003ekinematics, inertia\"]\n        T[\"Transmission\u003cbr/\u003eratio, backlash, friction\"]\n        C[\"Compliance\u003cbr/\u003estiffness, damping\"]\n    end\n    subgraph Electrical[\"Electrical domain\"]\n        A[\"Actuator\u003cbr/\u003etorque density, thermal\"]\n        S[\"Sensing\u003cbr/\u003eresolution, bandwidth, latency\"]\n    end\n    subgraph Compute[\"Computation\"]\n        P[\"Policy\u003cbr/\u003ePID / MPC / learned\"]\n    end\n\n    M --\u003e T --\u003e A --\u003e P\n    S --\u003e P --\u003e A\n    C -.-\u003e|\"sets achievable\u003cbr/\u003econtrol bandwidth\"| P\n    P -.-\u003e|\"required bandwidth\u003cbr/\u003econstrains design\"| C\n\n    style Physical fill:#e8f0fe\n    style Electrical fill:#fef3e8\n    style Compute fill:#e8fae8\n```\n\nThree rules that follow, and that no amount of learning removes:\n\n1. **Structural resonance caps your bandwidth.** A closed loop cannot be much faster than the first flexible mode of the structure it drives. Stiffness is a control-design parameter.\n2. **Backlash is not a disturbance, it is a discontinuity.** It breaks gradient-based tuning, breaks learned policies trained in simulation, and shows up as limit cycles.\n3. **Reflected inertia scales with gear ratio squared.** This is why quasi-direct-drive exists, and why highly geared arms cannot do impedance control well.\n\n### 3.1 Actuation \u0026 transmission\n\n- 📄 [Compact Gearboxes for Modern Robotics: A Review](https://www.frontiersin.org/articles/10.3389/frobt.2020.00103/full) — García et al., *Frontiers in Robotics and AI*, 2020. 🆓 **The best free survey of harmonic, cycloidal, planetary and novel drives, with efficiency and backdrivability compared.** ⭐\n- 📄 [Quasi-Direct Drive Actuation for a Lightweight Hip Exoskeleton with High Backdrivability and High Bandwidth](https://arxiv.org/abs/2004.00467) — Yu et al. 🆓 The clearest worked example of QDD design trade-offs.\n- 📄 [Cycloidal Quasi-Direct Drive Actuators with Learning-Based Torque Estimation](https://arxiv.org/abs/2410.16591) — 2024. 🆓 Where mechanical design and learning meet directly: use a network to recover torque a cheap transmission can't sense.\n- 📄 [Variable Impedance Actuators: A Review](https://www.sciencedirect.com/science/article/pii/S0921889013001188) — Vanderborght et al. The taxonomy paper for SEA / VSA / PEA.\n- 🔧 [SimpleFOC](https://simplefoc.com/) 🆓 — Field-oriented control on Arduino-class hardware. The fastest way to understand BLDC control by doing.\n- 🔧 [ODrive](https://odriverobotics.com/) 💵 / 🔧 [moteus](https://mjbots.com/) 💵 — Open(ish) high-performance BLDC controllers used across the hobby-to-research spectrum.\n- 🔧 [TMotor / CubeMars AK-series](https://www.cubemars.com/) 💵 — the de-facto QDD actuators for legged robot builds.\n\n**Selection cheat-sheet:**\n\n| Need | Architecture | Typical ratio | Trade-off |\n|---|---|---|---|\n| Precise position, high stiffness | Harmonic / strain wave | 50:1–160:1 | Non-backdrivable, expensive, torque ripple |\n| High dynamics, force control, impact | Quasi-direct drive (QDD) | 6:1–10:1 | Large motor, high current, heat |\n| Safe human contact, energy storage | Series elastic (SEA) | any + spring | Bandwidth limited by spring, extra sensing |\n| Cheap, high ratio, tolerant | Cycloidal | 20:1–100:1 | Vibration, harder to manufacture well |\n| Lightweight distal mass | Cable / tendon drive | varies | Friction, stretch, routing complexity |\n\n### 3.2 Compliance, contact and impedance\n\nLearning-based manipulation made compliance fashionable again: policies that touch things need hardware that can survive touching things.\n\n- 📄 [Series Elastic Actuators](https://ieeexplore.ieee.org/document/525827) — Pratt \u0026 Williamson, IROS 1995. The origin paper; still worth reading.\n- 📄 [Backdrivable actuators in robotics](https://www.emergentmind.com/topics/backdrivable-actuators) — a good living survey of the design space.\n- 📄 [Mechatronic whole-body co-design of a quadruped robot integrating local compliance](https://auctus-team.gitlabpages.inria.fr/jobs/2026/phd_quadruped/) — INRIA, 2026. States the current frontier plainly: rigid QDD quadrupeds have plateaued far below biological performance, and distributed compliance is the next lever.\n- 🔧 [Drake](https://drake.mit.edu/) 🆓 — hydroelastic contact modelling; the reference implementation for contact-rich simulation you can trust.\n\n### 3.3 Mechanical intelligence \u0026 morphological computation 🔬\n\nThe idea that the body itself performs computation — that a well-designed gripper needs less control than a badly-designed one. This has moved from curiosity to an active research programme.\n\n- 📄 [Intelligence as Computation](https://arxiv.org/abs/2412.14701) — Oliver Brock, 2024. Unifies digital, analog, mechanical and morphological computation.\n- 📄 [Perspectives on Intelligence in Soft Robotics](https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202400294) — Kortman et al., *Advanced Intelligent Systems*, 2025. 🆓 Classifies embodied intelligence into adaptive shape, adaptive functionality and adaptive mechanics.\n- 📄 [Reprogrammable metamaterial robot with embodied versatile computation and mechanical intelligence](https://www.nature.com/articles/s41467-026-71368-1) — *Nature Communications*, 2026. 🆓 Elastic-wave metamaterials performing analog and logic operations inside a crawling robot.\n- 📄 [The 2024 Active Metamaterials Roadmap](https://arxiv.org/abs/2411.09711) — 🆓 Shape-morphing metamaterials as embodied intelligence.\n- 📄 [Exploring Embodied Intelligence in Soft Robotics: A Review](https://www.mdpi.com/2313-7673/9/4/248) — *Biomimetics*, 2024. 🆓\n- 📄 [Morphological design methodologies of soft robots](https://www.the-innovation.org/article/doi/10.59717/j.xinn-inform.2025.100012) — 2025. 🆓 Forward biomimetic vs. inverse morphological design.\n\n### 3.4 Design, simulation and manufacturing tools\n\n| Tool | Type | Cost | Note |\n|---|---|---|---|\n| [FreeCAD](https://www.freecad.org/) | Parametric CAD | 🆓 | v1.x finally fixed the topological naming problem; genuinely usable now |\n| [Onshape](https://www.onshape.com/) | Cloud CAD | 🆓 free tier | Free for public documents; excellent for open-source hardware |\n| [SolidWorks](https://www.solidworks.com/) | CAD + CAE | 💵 | Industry default; motion + FEA add-ins |\n| [Fusion](https://www.autodesk.com/products/fusion-360/) | CAD/CAM/CAE | 💵 (free personal) | Integrated generative design and CAM |\n| [nTop](https://www.ntop.com/) | Implicit modelling | 💵 | Lattices, topology optimisation, field-driven design |\n| [Ansys](https://www.ansys.com/) / [COMSOL](https://www.comsol.com/) | Multiphysics FEA | 💵 | Thermal + structural + electromagnetic coupling |\n| [CalculiX](https://www.calculix.de/) / [Code_Aster](https://code-aster.org/) | FEA solver | 🆓 | Free FEA; usable via FreeCAD FEM workbench |\n| [ToOptix](https://github.com/DerBaumeister/toOptix) / [ToPy](https://github.com/williamhunter/topy) | Topology optimisation | 🆓 | Learn the method before paying for a suite |\n| [OpenModelica](https://openmodelica.org/) | Acausal multi-domain sim | 🆓 | The right tool for mechanical+hydraulic+electrical system models |\n| [Simscape](https://www.mathworks.com/products/simscape.html) | Multi-domain sim | 💵 | The commercial equivalent, tightly coupled to Simulink |\n| [Blender](https://www.blender.org/) | 3D modelling | 🆓 | Not CAD, but the standard for robot visual meshes and rendering |\n| [PrusaSlicer](https://www.prusa3d.com/prusaslicer/) / [OrcaSlicer](https://github.com/SoftFever/OrcaSlicer) | Slicers | 🆓 | Printed robot parts: print orientation determines layer-direction strength |\n\n### 3.5 Practical mechanical checklist for mechatronic builds 🧪\n\n\u003cdetails\u003e\n\u003csummary\u003eExpand — the things that actually go wrong\u003c/summary\u003e\n\n- **Actuator sizing:** size on RMS torque over the duty cycle, not peak. Check thermal, then check peak, then check backdrive torque.\n- **Reflected inertia ratio:** aim for load-to-motor inertia below ~10:1 for good servo response; below 3:1 for high dynamics.\n- **First resonance:** measure it (tap test + accelerometer, or a swept-sine on the actuator). Target closed-loop bandwidth ≤ 1/3 of it.\n- **Backlash budget:** total it across every joint in the chain. Preload, use anti-backlash gears, or move the encoder to the output.\n- **Encoder placement:** motor-side encoders lie about the load. Output-side encoders (dual encoding) cost more and solve most repeatability complaints.\n- **Cable management:** the leading cause of field failures in articulated robots. Design the cable path before the last link.\n- **Thermal path:** motors derate. Where does the heat go? Aluminium bracket, not printed PLA.\n- **Tolerance stack-up:** run it for the gripper-to-camera chain specifically — that's what a learned policy actually sees.\n- **Learnability:** if you plan to collect demonstrations, the robot must be backdrivable enough to hand-guide, repeatable enough that yesterday's data still applies, and mechanically identical to any other unit you deploy on.\n\n\u003c/details\u003e\n\n---\n\n## 4. Electronics, Embedded \u0026 Edge\n\n### Microcontrollers \u0026 compute\n\n| Platform | Role | Note |\n|---|---|---|\n| [Arduino](https://www.arduino.cc/) 🆓 | Learning, prototyping | The Uno R4 / Nano ESP32 generation is far more capable than the AVR days |\n| [ESP32 family](https://www.espressif.com/) 🆓 | Wireless mechatronics | Wi-Fi/BLE + dual core + enough RAM for micro-ROS |\n| [Raspberry Pi Pico / RP2350](https://www.raspberrypi.com/products/rp2350/) 🆓 | Real-time IO | PIO state machines are excellent for encoder decoding and step generation |\n| [STM32](https://www.st.com/en/microcontrollers-microprocessors/stm32-32-bit-arm-cortex-mcus.html) 💵 | Production motor control | The industry default for motor drives; G4/H7 for FOC |\n| [Teensy 4.x](https://www.pjrc.com/teensy/) 💵 | High-rate control loops | 600 MHz Cortex-M7; underrated for 10 kHz+ loops |\n| [Raspberry Pi 5](https://www.raspberrypi.com/) 💵 | Linux + ROS 2 | Enough for perception at modest rates; add a Hailo or Coral for NN inference |\n| [NVIDIA Jetson Orin / Thor](https://developer.nvidia.com/embedded-computing) 💵 | On-robot policy inference | Thor / T4000 (Blackwell, 2026) is what current VLAs are deployed on |\n\n### Firmware \u0026 RTOS\n\n- 🔧 [Zephyr RTOS](https://zephyrproject.org/) 🆓 — the RTOS to learn now: vendor-neutral, device-tree based, Linux-Foundation governed, huge board support.\n- 🔧 [FreeRTOS](https://www.freertos.org/) 🆓 — still ubiquitous, simpler mental model.\n- 🔧 [micro-ROS](https://micro.ros.org/) 🆓 — ROS 2 on microcontrollers. Runs on Zephyr, FreeRTOS, Mbed, Arduino. **The correct way to bridge MCU sensors/actuators into a ROS 2 system.** ⭐\n- 🔧 [PlatformIO](https://platformio.org/) 🆓 — sane multi-board build system; escape from the Arduino IDE.\n- 🔧 [Embassy](https://embassy.dev/) / [`embedded-hal`](https://github.com/rust-embedded/embedded-hal) 🆓 — async embedded Rust. Increasingly serious for safety-relevant firmware; memory safety without a GC.\n- 🔧 [Renode](https://renode.io/) 🆓 — emulate the whole board in CI. Test firmware without hardware.\n\n### Edge AI / TinyML\n\nRunning inference on the machine rather than in the cloud is now a normal part of a mechatronic design, especially for condition monitoring and anomaly detection.\n\n- 🔧 [Edge Impulse](https://edgeimpulse.com/) 💵/free tier — end-to-end TinyML: data capture → training → C++ export.\n- 🔧 [TensorFlow Lite for Microcontrollers / LiteRT](https://ai.google.dev/edge/litert/microcontrollers/overview) 🆓\n- 🔧 [microTVM](https://tvm.apache.org/docs/topic/microtvm/index.html) 🆓 — compiler-based deployment, better control over memory.\n- 🔧 [MicroFlow](https://github.com/matteocarnelos/microflow-rs) 🆓 — Rust TinyML inference engine; runs NNs on 8-bit MCUs with 2 kB RAM. 📄 [paper](https://arxiv.org/abs/2409.19432)\n- 📄 [State of Edge AI on Microcontrollers in 2026](https://shawnhymel.com/3125/state-of-edge-ai-on-microcontrollers-in-2026/) — Shawn Hymel. A clear-eyed, hype-free assessment; note the trend of silicon vendors absorbing the tooling layer.\n- 📖 [TinyML](https://www.oreilly.com/library/view/tinyml/9781492052036/) — Warden \u0026 Situnayake 💵, and [TinyML Cookbook](https://www.packtpub.com/product/tinyml-cookbook-second-edition/9781837637362) 💵\n\n### Electronics design\n\n- 🔧 [KiCad](https://www.kicad.org/) 🆓 — v8/v9 is fully production-capable. No reason to pay for hobby or small-team PCB work.\n- 🔧 [Fritzing](https://fritzing.org/) 💵 (small fee) — still the best for teaching wiring diagrams.\n- 🔧 [Proteus](https://www.labcenter.com/) 💵 — schematic capture with MCU co-simulation.\n- 🔧 [Falstad Circuit Simulator](https://www.falstad.com/circuit/) 🆓 — instant intuition for analog circuits, in the browser.\n- 🔧 [LTspice](https://www.analog.com/en/resources/design-tools-and-calculators/ltspice-simulator.html) 🆓 — free, accurate SPICE; use it before you build the motor driver.\n- 🔧 [LabVIEW](https://www.ni.com/labview) 💵 — still dominant in test \u0026 measurement rigs.\n- 📖 [The Art of Electronics](https://artofelectronics.net/) — Horowitz \u0026 Hill, 3rd ed. 💵 The reference.\n- 📖 [Practical Electronics for Inventors](https://www.mhprofessional.com/practical-electronics-for-inventors-fifth-edition-9781264268856-usa) — Scherz \u0026 Monk. 💵 More approachable starting point.\n\n---\n\n## 5. Industrial Automation, Industry 4.0 / 5.0\n\n### Controllers \u0026 languages\n\n- [IEC 61131-3](https://en.wikipedia.org/wiki/IEC_61131-3) — the PLC languages standard (LD, FBD, ST, IL, SFC). Learn **Structured Text** first; ladder second.\n- [IEC 61499](https://en.wikipedia.org/wiki/IEC_61499) — distributed, event-driven automation. The intended successor for distributed control; slow adoption but conceptually important. 🔧 [Eclipse 4diac](https://eclipse.dev/4diac/) 🆓 is the open reference implementation.\n- 🔧 [OpenPLC](https://autonomylogic.com/) 🆓 — open-source IEC 61131-3 runtime + editor. **The cheapest possible way to learn real PLC programming.** ⭐ 🧪\n- 🔧 [Beremiz](https://beremiz.org/) 🆓 — open IDE for IEC 61131-3.\n- 🔧 [CODESYS](https://www.codesys.com/) 💵 — the vendor-neutral runtime behind many PLC brands.\n- 🔧 [TwinCAT](https://www.beckhoff.com/twincat/) 💵 — Beckhoff's PC-based control platform; increasingly the platform where AI-in-automation experiments happen first.\n- 🔧 [Siemens TIA Portal / STEP 7](https://www.siemens.com/global/en/products/automation/industry-software/automation-software/tia-portal.html) 💵\n- 📖 [Programmable Logic Controllers](https://www.mheducation.com/highered/product/programmable-logic-controllers-petruzella/M9780073373843.html) — Petruzella. 💵 The standard PLC textbook.\n\n### Simulation \u0026 commissioning\n\n- 🔧 [Factory I/O](https://factoryio.com/) 💵 — 3D factory simulation you can drive from a real PLC. Best-in-class for learning. 🧪\n- 🔧 [NVIDIA Isaac Sim / Omniverse](https://developer.nvidia.com/isaac/sim) 🆓 — OpenUSD-based digital twins of full production cells.\n- 🔧 [Visual Components](https://www.visualcomponents.com/) 💵 / [Process Simulate](https://plm.sw.siemens.com/en-US/tecnomatix/products/process-simulate-software/) 💵 — commercial virtual commissioning.\n- 🔧 [Ignition](https://inductiveautomation.com/) 💵 — SCADA/MES platform with a very good free trial mode for learning.\n\n### Connectivity, information models and digital twins\n\nThis is the part of Industry 4.0 that actually matters and that most curricula skip.\n\n- 🔧 [OPC UA](https://opcfoundation.org/) — the industrial information-model standard. Not just a protocol: it's a type system for machines. 🆓 [open62541](https://www.open62541.org/) is the reference open-source stack.\n- 🔧 [Asset Administration Shell (AAS)](https://industrialdigitaltwin.org/) — **IEC 63278-1:2023**. The standardised digital twin. 🆓 [Eclipse BaSyx](https://basyx.org/) and [AASX Package Explorer](https://github.com/admin-shell-io/aasx-package-explorer) are the tools to start with.\n- 🔧 [MQTT](https://mqtt.org/) + [Sparkplug B](https://sparkplug.eclipse.org/) 🆓 — the pragmatic OT→IT data path; Sparkplug adds state and payload conventions MQTT lacks.\n- 🔧 [AutomationML (IEC 62714)](https://www.automationml.org/) 🆓 — engineering data exchange between CAD/PLC/robot tools.\n- [RAMI 4.0](https://www.plattform-i40.de/) — the reference architecture model that ties the above together.\n- **Digital Product Passport** — the AAS + OPC UA convergence now driving EU regulatory work; [open-source reference tooling exists](https://www.digitaltwinconsortium.org/2025/04/opc-ua-apps-and-services-to-build-digital-product-passports-now-available-open-source/).\n- 📄 [Asset Administration Shell in Manufacturing: Applications and Relationship with Digital Twin](https://www.sciencedirect.com/science/article/pii/S2405896322020997) 🆓\n\n### Industry 5.0 concepts\n\n- **Human Cyber-Physical Systems (HCPS)** — the human is inside the control loop by design, not by exception.\n- **Cobots** — ISO/TS 15066 defines the power-and-force-limiting regime; read it before designing any human-adjacent machine.\n- **Ecomechatronics** — energy- and material-efficiency as first-class design objectives, driven by EU sustainability regulation.\n- 📄 [The Evolution of Mechatronics Engineering and Its Relationship with Industry 3.0, 4.0, and 5.0](https://doi.org/10.3390/technologies14020081) — 2026. 🆓\n\n---\n\n## 6. The Robotics Software Stack\n\n### ROS 2 — current state (as of mid-2026)\n\n| Distro | Released | Ubuntu | Support until | Use it? |\n|---|---|---|---|---|\n| **Lyrical Luth** | May 2026 | 26.04 | May 2031 | ✅ New production projects (LTS) |\n| Kilted Kaiju | May 2025 | 24.04 | Nov 2026 | ⚠️ Migrate off |\n| **Jazzy Jalisco** | May 2024 | 24.04 | May 2029 | ✅ Safe, widest package support today |\n| Humble Hawksbill | May 2022 | 22.04 | May 2027 | ⚠️ Plan migration |\n\n*ROS 1 reached end of life in May 2025. New projects should not use it.*\n\n- 🎓 [ROS 2 official tutorials](https://docs.ros.org/) 🆓 ⭐\n- 🎓 [ROS 2 for Beginners](https://roboticsbackend.com/) — Edouard Renard 💵 / lots of free material\n- 🔧 [MoveIt 2](https://moveit.picknik.ai/) 🆓 — motion planning for manipulators\n- 🔧 [Nav2](https://docs.nav2.org/) 🆓 — the navigation stack for mobile robots\n- 🔧 [ros2_control](https://control.ros.org/) 🆓 — hardware abstraction + controller lifecycle. Learn this before writing a custom driver.\n- 🔧 [Zenoh](https://zenoh.io/) 🆓 — increasingly used as an alternative RMW / bridge, especially over lossy links.\n\n### Kinematics, dynamics \u0026 optimisation libraries\n\n- 🔧 [Pinocchio](https://github.com/stack-of-tasks/pinocchio) 🆓 — fast rigid-body dynamics with analytical derivatives. The backbone of most modern whole-body controllers.\n- 🔧 [Drake](https://drake.mit.edu/) 🆓 — MIT's toolbox: modelling, contact, trajectory optimisation, convex programs. 🔬\n- 🔧 [CasADi](https://web.casadi.org/) 🆓 — symbolic framework for nonlinear optimisation and optimal control.\n- 🔧 [acados](https://docs.acados.org/) 🆓 — embedded NMPC that actually runs at kHz rates on real hardware.\n- 🔧 [Crocoddyl](https://github.com/loco-3d/crocoddyl) 🆓 — DDP-family optimal control for legged/multi-contact robots. 🔬\n- 🔧 [OMPL](https://ompl.kavrakilab.org/) 🆓 — sampling-based motion planning.\n- 🔧 [Python Robotics](https://github.com/AtsushiSakai/PythonRobotics) 🆓 — readable implementations of dozens of algorithms. **Excellent for learning.** ⭐\n\n### Simulation — this changed completely in 2025–2026\n\nThe physics-engine landscape has been rewritten by GPU acceleration.\n\n| Simulator | Cost | Best for |\n|---|---|---|\n| [**Newton**](https://github.com/newton-physics/newton) | 🆓 | The new centre of gravity. Open-source, GPU-accelerated, differentiable; built on NVIDIA Warp + OpenUSD; developed by NVIDIA + Google DeepMind + Disney Research under the Linux Foundation. v1.0 GA at GTC 2026. Bundles MuJoCo-Warp and Disney's Kamino (closed-loop mechanisms) solvers, SDF collision, hydroelastic contact, and deformables. |\n| [MuJoCo](https://mujoco.org/) | 🆓 | Contact-rich control research; CPU version remains the easiest to debug |\n| [MuJoCo Playground](https://github.com/google-deepmind/mujoco_playground) | 🆓 | Ready-made GPU RL environments |\n| [Isaac Lab](https://github.com/isaac-sim/IsaacLab) | 🆓 | Large-scale robot learning; v3.0 builds on Newton + PhysX |\n| [Isaac Sim](https://developer.nvidia.com/isaac/sim) | 🆓 | Photorealistic digital twins, synthetic data, sensor simulation |\n| [Genesis](https://github.com/Genesis-Embodied-AI/Genesis) | 🆓 | Fast multi-platform GPU physics; strong on generative scene creation |\n| [Gazebo](https://gazebosim.org/) | 🆓 | ROS-native system-level simulation; still the right tool for full-robot integration testing |\n| [Webots](https://cyberbotics.com/) | 🆓 | Education; batteries included, low setup cost ⭐ |\n| [CoppeliaSim](https://www.coppeliarobotics.com/) | 🆓 edu | Teaching kinematics; used by the Modern Robotics course |\n| [SAPIEN](https://sapien.ucsd.edu/) | 🆓 | Articulated-object manipulation research |\n\n**Practical guidance:** for *learning a policy*, use Newton/MuJoCo-Warp or Isaac Lab. For *validating a system*, use Gazebo or Isaac Sim. For *understanding what your controller does*, use MuJoCo on CPU with the viewer open.\n\n### Perception\n\n- 🔧 [OpenCV](https://opencv.org/) 🆓 — v5 launched at CVPR 2026.\n- 🔧 [Open3D](https://www.open3d.org/) 🆓 — point clouds and 3D processing.\n- 🔧 [SAM 2](https://github.com/facebookresearch/sam2) 🆓 — promptable segmentation for images and video; now a standard preprocessing block in robot perception.\n- 🔧 [FoundationPose](https://github.com/NVlabs/FoundationPose) 🆓 — 6-DoF pose estimation for novel objects.\n- 🔧 [ORB-SLAM3](https://github.com/UZ-SLAMLab/ORB_SLAM3) 🆓 / [RTAB-Map](https://introlab.github.io/rtabmap/) 🆓 — visual and RGB-D SLAM.\n- 🔧 [Nerfstudio](https://docs.nerf.studio/) 🆓 / [gsplat](https://github.com/nerfstudio-project/gsplat) 🆓 — NeRF and 3D Gaussian splatting; now used for real-to-sim asset capture.\n\n### Tooling \u0026 visualisation\n\n- 🔧 [Foxglove](https://foxglove.dev/) 🆓 free tier — the modern replacement for RViz+rqt for log inspection.\n- 🔧 [Rerun](https://rerun.io/) 🆓 — multimodal time-series visualisation. Excellent for debugging learned policies (log observations, actions and predictions together).\n- 🔧 [PlotJuggler](https://plotjuggler.io/) 🆓 — the fastest way to look at time-series from a robot or PLC.\n- 🔧 [MCAP](https://mcap.dev/) 🆓 — the modern robotics log format.\n\n---\n\n## 7. Learning-Based Control: The New Core\n\nThis is the section that did not exist when this list was first written. In five years, robot manipulation moved from \"write an inverse-kinematics solver and a state machine\" to \"collect demonstrations and train a policy.\" Both approaches are alive; a mechatronic engineer in 2026 needs to know when to reach for which.\n\n### 7.0 The mental model\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/classical-vs-learned-pipeline.svg\" width=\"900\" alt=\"Side-by-side comparison of the classical model-based control pipeline and the learned visuomotor pipeline, with guidance on when to use each\"\u003e\n\u003c/p\u003e\n\n```mermaid\nflowchart LR\n    subgraph Classical[\"Classical mechatronic pipeline\"]\n        direction TB\n        C1[\"Sensors\"] --\u003e C2[\"State estimation\"] --\u003e C3[\"Planner\"] --\u003e C4[\"Controller\u003cbr/\u003ePID / MPC\"] --\u003e C5[\"Actuators\"]\n    end\n\n    subgraph Learned[\"Learned visuomotor pipeline\"]\n        direction TB\n        L1[\"Cameras +\u003cbr/\u003eproprioception\"] --\u003e L2[\"Neural policy\u003cbr/\u003eACT · Diffusion · VLA\"] --\u003e L3[\"Action chunk\u003cbr/\u003e(next N actions)\"] --\u003e L4[\"Low-level\u003cbr/\u003ejoint controller\"] --\u003e L5[\"Actuators\"]\n    end\n\n    Choose{\"Which one?\"}\n    Classical -.-\u003e|\"models known,\u003cbr/\u003estate observable,\u003cbr/\u003esafety certifiable\"| Choose\n    Learned -.-\u003e|\"contact-rich,\u003cbr/\u003edeformable, cluttered,\u003cbr/\u003ehard to model\"| Choose\n\n    style Classical fill:#e8f0fe\n    style Learned fill:#e8fae8\n```\n\nThree ideas do most of the work in modern policies:\n\n1. **Action chunking** — predict a *sequence* of future actions instead of one. Cuts compounding error and makes the policy robust to slow inference.\n2. **Generative action heads** — model the *distribution* over action sequences (diffusion, flow matching) rather than regressing a mean. Critical when demonstrations are multimodal (two valid ways to grasp a mug, and averaging them drops the mug).\n3. **Pretrained vision-language backbones** — inherit semantic and spatial priors from internet-scale data so the robot generalises to objects and instructions it never saw.\n\n### 7.1 Imitation learning \u0026 action chunking\n\n- 📄 [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (ACT / ALOHA)](https://arxiv.org/abs/2304.13705) — Zhao, Kumar, Levine, Finn, RSS 2023. 🆓 **The paper that made cheap bimanual imitation learning work.** Start here. ⭐ [code](https://github.com/tonyzhaozh/act) · [project](https://tonyzhaozh.github.io/aloha/)\n- 📄 [Universal Manipulation Interface (UMI)](https://umi-gripper.github.io/) — Chi et al., RSS 2024. 🆓 Collect demonstrations with a handheld gripper and a GoPro, no robot required. A genuinely important idea for anyone without a robot budget. 🧪\n- 📄 [Action chunking and exploratory data collection yield exponential improvements in behavior cloning](https://arxiv.org/abs/2507.09061) — 2025. 🔬 The theory behind why chunking works.\n- 📄 [Bidirectional Decoding](https://arxiv.org/abs/2408.17355) — ICLR 2025. Closed-loop resampling to fix chunk-boundary artefacts.\n\n### 7.2 Diffusion policies\n\nA diffusion policy generates robot actions by iteratively denoising, exactly as image diffusion models generate pixels. It handles multimodal demonstrations naturally and has become the default strong baseline.\n\n- 📄 [**Diffusion Policy: Visuomotor Policy Learning via Action Diffusion**](https://diffusion-policy.cs.columbia.edu/) — Chi et al., RSS 2023 / *IJRR* 2025. 🆓 **The foundational paper.** ⭐ [code](https://github.com/real-stanford/diffusion_policy) · [arXiv](https://arxiv.org/abs/2303.04137)\n- 📄 [3D Diffusion Policy (DP3)](https://3d-diffusion-policy.github.io/) — point-cloud conditioning; large sample-efficiency gains.\n- 📄 [Consistency Policy](https://consistency-policy.github.io/) / [ManiCM](https://arxiv.org/abs/2406.01586) — distil the multi-step denoiser into few-step inference for real-time control.\n- 📄 [NoMaD: Goal-Masked Diffusion Policies for Navigation and Exploration](https://general-navigation-models.github.io/nomad/) — ICRA 2024. Diffusion for mobile robots, not just arms.\n- 📄 [Steering Your Diffusion Policy with Latent-Space RL](https://arxiv.org/abs/2506.15799) — CoRL 2025. 🔬 Improve a cloned policy with RL without retraining it end to end.\n- 📄 [Much Ado About Noising: Dispelling the Myths of Generative Robotic Control](https://arxiv.org/abs/2512.01809) — 2025. 🔬 A useful sceptical counterweight: read it before assuming diffusion is always the answer.\n\n### 7.3 Flow matching \u0026 real-time chunking\n\nFlow matching learns a deterministic transport from noise to data instead of an iterative denoising chain. In practice: faster inference, smoother trajectories, better stability — which is why the newest VLAs use it.\n\n- 📄 [Flow Matching for Generative Modeling](https://arxiv.org/abs/2210.02747) — Lipman et al., ICLR 2023. 🆓 The source.\n- 📄 [Real-Time Execution of Action Chunking Flow Policies (RTC)](https://arxiv.org/abs/2506.07339) — Black, Galliker, Levine, NeurIPS 2025. 🆓 \"Action inpainting\": compute the next chunk *while executing the current one*. **This is the paper that made big VLAs usable at real robot rates.** ⭐\n- 📄 [Training-Time Action Conditioning for Efficient Real-Time Chunking](https://arxiv.org/abs/2512.05964) — 2025.\n- 📄 [Streaming Flow Policy](https://arxiv.org/abs/2505.21851) — treat the action trajectory itself as the flow trajectory; stream actions out continuously.\n- 📄 [FlowPolicy](https://arxiv.org/abs/2412.04987) — consistency flow matching for fast 3D policies.\n- 📄 [FAST: Efficient Action Tokenization for VLAs](https://www.pi.website/research/fast) — Pertsch et al., RSS 2025. 🆓 DCT-based action tokenisation; makes autoregressive VLAs 5× faster to train.\n\n### 7.4 Vision-Language-Action models (VLAs)\n\nA VLA takes camera images plus a natural-language instruction and outputs robot actions, end to end. This is the fastest-moving area in robotics.\n\n```mermaid\nflowchart LR\n    IMG[\"Camera(s)\"] --\u003e VE[\"Vision encoder\u003cbr/\u003eSigLIP / DINOv2\"]\n    TXT[\"Instruction:\u003cbr/\u003e'put the mug in the sink'\"] --\u003e LM\n    VE --\u003e LM[\"Language model backbone\u003cbr/\u003eLlama / PaliGemma / Cosmos\"]\n    STATE[\"Proprioception\"] --\u003e AH\n    LM --\u003e AH[\"Action head\"]\n    AH --\u003e A1[\"Discrete action tokens\u003cbr/\u003e(autoregressive)\"]\n    AH --\u003e A2[\"Diffusion / flow\u003cbr/\u003eaction expert\"]\n    A1 --\u003e CH[\"Action chunk\u003cbr/\u003eH × DoF\"]\n    A2 --\u003e CH\n    CH --\u003e RTC[\"Real-time chunking\u003cbr/\u003e+ low-level controller\"]\n    RTC --\u003e ROBOT[\"Robot\"]\n    ROBOT --\u003e|\"new observation\"| IMG\n\n    style LM fill:#fef3e8\n    style AH fill:#e8fae8\n```\n\n**Open models you can actually run:**\n\n| Model | Params | Licence | Notes |\n|---|---|---|---|\n| [**SmolVLA**](https://huggingface.co/blog/smolvla) | 450M | 🆓 Apache | Hugging Face, June 2025. Trained purely on community datasets; runs on consumer hardware. **Best starting point.** ⭐ [📄](https://arxiv.org/abs/2506.01844) |\n| [**OpenVLA**](https://openvla.github.io/) | 7B | 🆓 | Stanford/Berkeley, 2024. ~970k Open X-Embodiment episodes; DINOv2 + SigLIP + Llama 2. The reference open VLA. [📄](https://arxiv.org/abs/2406.09246) [code](https://github.com/openvla/openvla) |\n| [OpenVLA-OFT](https://openvla-oft.github.io/) | 7B | 🆓 | Optimised fine-tuning recipe; large speed/success gains over base OpenVLA |\n| [**π₀ / openpi**](https://github.com/Physical-Intelligence/openpi) | ~3B | 🆓 weights | Physical Intelligence. Flow-matching action expert on a VLM backbone; pretrained on 10,000+ hours. Smoothest trajectories in contact-rich tasks. [📄](https://arxiv.org/abs/2410.24164) |\n| [**Isaac GR00T N**](https://github.com/NVIDIA/Isaac-GR00T) | ~2–3B | 🆓 | NVIDIA. Dual-system: slow VLM planner (System 2) + fast diffusion transformer controller (System 1). N1 (Mar 2025) → N1.5 → N1.6 (Dec 2025, Cosmos-2B backbone). Built for humanoids. [📄](https://arxiv.org/abs/2503.14734) |\n| [Octo](https://octo-models.github.io/) | 27M–93M | 🆓 | Generalist transformer policy; small and easy to fine-tune |\n| [SpatialVLA](https://spatialvla.github.io/) | 4B | 🆓 | Explicit 3D spatial representations |\n| [MolmoAct](https://arxiv.org/abs/2508.07917) | — | 🆓 | \"Action reasoning model\" — reasons in space before acting |\n| [BitVLA](https://arxiv.org/abs/2506.07530) | 3B | 🆓 | 1-bit weights; VLA inference on constrained hardware 🔬 |\n| [Gemini Robotics On-Device](https://deepmind.google/discover/blog/gemini-robotics-on-device-brings-ai-to-local-robotic-devices/) | — | 💵 restricted | Google DeepMind; on-robot inference without cloud |\n\n**Surveys — read one of these before the papers:**\n\n- 📄 [Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications](https://vla-survey.github.io/) — Kawaharazuka et al., *IEEE Access* 13:162467–162504, 2025. 🆓 **Best practical survey; includes a filterable database of every VLA.** ⭐\n- 📄 [A Survey on Vision-Language-Action Models for Embodied AI](https://arxiv.org/abs/2405.14093) — Ma et al., continuously updated through 2026.\n- 📄 [Vision-Language-Action Models: Concepts, Progress, Applications and Challenges](https://arxiv.org/abs/2505.04769) — Sapkota et al., 2025. 🆓 Good on architectural taxonomy.\n- 📄 [Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines](https://arxiv.org/abs/2604.23001) — TMLR 2026. 🆓 Argues the bottleneck is now **data infrastructure, not architecture** — the most important strategic claim in the field right now.\n- 📄 [A Survey on Efficient Vision-Language-Action Models](https://arxiv.org/abs/2510.24795) — 2025. Quantisation, token pruning, caching, distillation. Read this if you have to deploy on a Jetson.\n- 📄 [Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms](https://arxiv.org/abs/2604.23775) — 2026. 🔬\n\n### 7.5 World models 🔬\n\nLearned simulators that predict how the world evolves given actions — used for synthetic data generation, planning in imagination, and safe evaluation.\n\n- 🔧 [NVIDIA Cosmos](https://developer.nvidia.com/cosmos) 🆓 open weights — world foundation models for physical AI; Cosmos 3 (GTC 2026) unifies world generation, vision reasoning and action simulation.\n- 📄 [Genie 3](https://deepmind.google/discover/blog/genie-3-a-new-frontier-for-world-models/) — Google DeepMind. Real-time interactive world generation.\n- 📄 [DayDreamer / Dreamer V3](https://danijar.com/project/dreamerv3/) — model-based RL that learns a world model and plans inside it.\n- 📄 [Real2Render2Real](https://arxiv.org/abs/2505.11917) / [GigaBrain-0](https://arxiv.org/abs/2510.19430) — scaling robot data without scaling robot hardware.\n- **Why it matters for mechatronics:** synthetic data augmentation lets a team turn ~200 real demonstrations into thousands of variants, which is often cheaper than buying more robots.\n\n### 7.6 Reinforcement learning on real hardware\n\n- 📄 [HIL-SERL: Human-in-the-Loop Sample-Efficient RL](https://hil-serl.github.io/) — Luo et al. 🆓 Trains real-robot manipulation policies to near-perfect success in 1–2 hours of real interaction. **The most practically important real-robot RL result of recent years.** ⭐\n- 📄 [Learning Quadrupedal Locomotion over Challenging Terrain](https://leggedrobotics.github.io/rl-blindloco/) — Lee et al., *Science Robotics* 2020. The sim-to-real result that started the legged-robot RL era.\n- 📄 [Rapid Motor Adaptation](https://ashish-kmr.github.io/rma-legged-robots/) — online adaptation to changing dynamics.\n- 🔧 [Stable-Baselines3](https://stable-baselines3.readthedocs.io/) 🆓 / [CleanRL](https://docs.cleanrl.dev/) 🆓 — CleanRL's single-file implementations are the best way to actually understand the algorithms.\n- 🔧 [rsl_rl](https://github.com/leggedrobotics/rsl_rl) 🆓 — the PPO implementation behind most legged-robot papers.\n\n### 7.7 Datasets \u0026 benchmarks\n\n| Resource | What |\n|---|---|\n| [Open X-Embodiment](https://robotics-transformer-x.github.io/) 🆓 | 1M+ trajectories, 22 embodiments, 30+ labs. The ImageNet moment for robot data. |\n| [DROID](https://droid-dataset.github.io/) 🆓 | 76k in-the-wild manipulation trajectories, 564 scenes |\n| [BridgeData V2](https://rail-berkeley.github.io/bridgedata/) 🆓 | 60k trajectories, widely used for VLA pretraining |\n| [LeRobot datasets on the HF Hub](https://huggingface.co/datasets?other=LeRobot) 🆓 | Thousands of community datasets in a standard format |\n| [LIBERO](https://libero-project.github.io/) 🆓 | The standard lifelong-manipulation benchmark |\n| [SimplerEnv](https://github.com/simpler-env/SimplerEnv) 🆓 | Reproducible simulated evaluation of real-robot VLAs |\n| [RoboCasa](https://robocasa.ai/) 🆓 | Large-scale simulated kitchen environments |\n| [RoboTwin 2.0](https://arxiv.org/abs/2506.18088) 🆓 | Bimanual manipulation data generator + benchmark |\n| [Isaac Lab-Arena](https://developer.nvidia.com/isaac/lab) 🆓 | NVIDIA's robot evaluation framework (2026) |\n\n### 7.8 Hands-on: train your own policy 🧪\n\nThe single highest-value practical exercise in modern mechatronics. Under €500 of hardware and one afternoon.\n\n- 🔧 [**LeRobot**](https://github.com/huggingface/lerobot) 🆓 — Hugging Face's end-to-end robot learning library. `pip install`, no ROS required. Includes ACT, Diffusion Policy, VQ-BeT, π₀, SmolVLA, HIL-SERL, TD-MPC. ⭐ [📄 ICLR 2026](https://arxiv.org/abs/2602.22818)\n- 📖 [**Robot Learning: A Tutorial**](https://arxiv.org/abs/2510.12403) — Capuano, Pascal, Zouitine, Aractingi, Wolf. 🆓 RL → behavioural cloning → generalist policies, with runnable `lerobot` code. **Read this cover to cover.** ⭐\n- 🎓 [SO-101 assembly guide](https://huggingface.co/docs/lerobot/so101) 🆓 — 3D-printable leader/follower arm pair, ~€120–250 depending on servos.\n- 🎓 [NVIDIA SO-101 sim-to-real course](https://docs.nvidia.com/learning/physical-ai/sim-to-real-so-101/latest/) 🆓 — the same arm, through Isaac Sim → Isaac Lab → GR00T → hardware.\n- 📝 [How I trained ACT on SO-101: journey, gotchas and lessons](https://huggingface.co/blog/sherryxychen/train-act-on-so-101) 🆓 — honest write-up of the failure modes (no eval split, accidentally cheating by watching the arm instead of the camera feed). Read it *before* you start.\n\n**Realistic expectations:** ~50 demonstrations and ~30 minutes on an RTX 3060 gets a working single-task ACT policy. Language conditioning and generalisation need far more. For a single fixed task, ACT or Diffusion Policy usually beats a general VLA — reach for a VLA when you need language conditioning or cross-task transfer.\n\n---\n\n## 8. Agentic AI in Automation and Robotics\n\n\"Agentic\" is the most abused word in industrial marketing right now, so start with the distinction that actually matters:\n\n\u003e **A copilot answers when asked. An agent acts on a trigger, plans a sequence, calls tools, and escalates only when confidence is low or a threshold is crossed.**\n\n```mermaid\nflowchart TD\n    G[\"Goal\u003cbr/\u003e'keep OEE above 85% this shift'\"] --\u003e P[\"Planner\u003cbr/\u003eLLM reasoning\"]\n    P --\u003e T{\"Tool selection\"}\n    T --\u003e T1[\"MES / ERP API\"]\n    T --\u003e T2[\"Historian / time-series DB\"]\n    T --\u003e T3[\"OPC UA client\u003cbr/\u003eread tags\"]\n    T --\u003e T4[\"CMMS\u003cbr/\u003ecreate work order\"]\n    T1 \u0026 T2 \u0026 T3 \u0026 T4 --\u003e O[\"Observation\"]\n    O --\u003e P\n    P --\u003e G2{\"Confidence and\u003cbr/\u003eauthority check\"}\n    G2 --\u003e|\"within limits\"| ACT[\"Bounded actuation\u003cbr/\u003esmall setpoint change\"]\n    G2 --\u003e|\"outside limits\"| HUM[\"Human-in-the-loop\u003cbr/\u003eapproval\"]\n    ACT --\u003e SAFETY[\"Deterministic safety layer\u003cbr/\u003ePLC interlocks · ISO 13849 · SIL\"]\n    HUM --\u003e SAFETY\n    SAFETY --\u003e MACHINE[\"Machine\"]\n\n    style SAFETY fill:#fde8e8\n    style HUM fill:#fef3e8\n```\n\n**The non-negotiable design rule:** the LLM never sits inside the safety function. Interlocks, e-stops and safety-rated logic remain deterministic, certified and independent. An agent may adjust a setpoint inside a human-defined envelope; it may not define the envelope.\n\n### 8.1 Where this actually is, in production\n\n- **Beckhoff TwinCAT CoAgent** (Hannover Messe 2026) — LLMs connected over the **Model Context Protocol** driving real machine motion sequences inside the TwinCAT platform. Engineers describe a motion in natural language; the platform generates and runs it.\n- **Dell / XMPro / NVIDIA Omniverse** — live PLC data from a brewery centrifuge digital twin feeding an LLM that detects boundary violations and makes small supervised SCADA-level adjustments. Deployed, not a proof of concept.\n- **Siemens Industrial Copilot** — code generation and diagnostics inside TIA Portal.\n- **Adoption reality check:** surveys through 2026 put most manufacturers at pilot stage, a minority at line-level deployment, and only a few percent letting agents make consequential decisions unsupervised. Multi-agent orchestration is still rare. Human-in-the-loop remains a hard requirement in regulated and high-precision environments, because non-deterministic behaviour is a validation problem, not just a quality problem.\n\n### 8.2 Protocols and building blocks\n\n- 🔧 [**Model Context Protocol (MCP)**](https://modelcontextprotocol.io/) 🆓 — the open standard for connecting models to tools and data sources. Rapidly becoming the way agents reach OPC UA servers, historians and MES.\n- 🔧 [Agent2Agent (A2A)](https://a2a-protocol.org/) 🆓 — inter-agent interoperability across vendors.\n- 🔧 [LangGraph](https://www.langchain.com/langgraph) 🆓 / [CrewAI](https://www.crewai.com/) 🆓 / [AutoGen](https://microsoft.github.io/autogen/) 🆓 — agent orchestration frameworks.\n- 🔧 [Node-RED](https://nodered.org/) 🆓 — unglamorous, but still the most practical glue between OT protocols and everything else.\n\n### 8.3 Agents in robotics (as opposed to plant floors)\n\n- 📄 [Code as Policies](https://code-as-policies.github.io/) — LLMs writing robot control code. The paper that opened this line.\n- 📄 [SayCan](https://say-can.github.io/) — grounding language plans in what a robot can actually do.\n- 📄 [Embodied Chain-of-Thought Reasoning](https://embodied-cot.github.io/) — CoRL 2024. Reasoning traces improve VLA action quality.\n- 📄 [OpenHelix](https://arxiv.org/abs/2505.03912) — open dual-system (planner + controller) VLA; a good template for the architecture. 🆓\n- The dual-system pattern (slow LLM planner + fast reactive controller) is now the dominant architecture — GR00T N1's System 1 / System 2 split is the clearest published example.\n\n### 8.4 Safety, assurance and governance\n\n- 📄 [Agentic AI in Engineering and Manufacturing](https://decode.mit.edu/assets/papers/2026_Edwards_Agentic_AI_in_Engineering_and_Manufacturing.pdf) — MIT DeCoDE Lab, 2026. 🆓 Sober analysis of *bounded autonomy*: agents inside tightly scoped workflows, subject to human validation, not assuming engineering accountability. ⭐\n- [EU AI Act](https://artificialintelligenceact.eu/) — machinery and safety components fall under high-risk obligations. If your agent touches a machine sold in the EU, this applies to you.\n- [EU Machinery Regulation 2023/1230](https://eur-lex.europa.eu/eli/reg/2023/1230/oj) — replaces the Machinery Directive from January 2027 and explicitly addresses self-evolving behaviour and AI-enabled safety components. **This is the regulation mechatronic engineers should be reading now.**\n- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) 🆓\n- [OWASP Top 10 for LLM Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/) 🆓 — prompt injection into an agent with OPC UA write access is not a theoretical risk.\n- ISO 10218-1/-2:2025 (industrial robot safety, revised) and ISO/TS 15066 (collaborative operation).\n\n---\n\n## 9. Hands-On Projects\n\nOrdered by difficulty. Each one teaches something the previous one couldn't.\n\n| # | Project | Level | Rough cost | What it actually teaches |\n|---|---|---|---|---|\n| 1 | Closed-loop DC motor position control with encoder | Beginner | €30 | Sampling, quantisation, integral windup, derivative noise |\n| 2 | Line follower with PID on IR array | Beginner | €40 | Sensor calibration, loop rate vs. speed, saturation |\n| 3 | Reaction wheel / inverted pendulum | Beginner+ | €60 | Unstable plants, state feedback, why LQR exists 🧪 |\n| 4 | BLDC field-oriented control with SimpleFOC | Intermediate | €80 | Commutation, current control, why FOC beats trapezoidal |\n| 5 | PLC-controlled sorting line in Factory I/O | Intermediate | €30 (licence) | Ladder/ST, sequence control, HMI, industrial thinking |\n| 6 | ESP32 + micro-ROS sensor node into a ROS 2 graph | Intermediate | €20 | Distributed robotics, QoS, real-time boundaries |\n| 7 | Differential-drive robot: SLAM + Nav2 | Intermediate+ | €200 | TF trees, odometry drift, costmaps, localisation |\n| 8 | 3D-print an SO-101 arm, teleoperate it | Intermediate+ | €150–250 | Servo calibration, leader-follower, mechanical repeatability |\n| 9 | **Collect 50 demos, train ACT, run it on the SO-101** | Intermediate+ | +GPU access | Data quality, overfitting, the whole modern paradigm ⭐ |\n| 10 | Fine-tune SmolVLA on your own task | Advanced | +GPU | Language conditioning, LoRA, evaluation protocol |\n| 11 | RL locomotion in Isaac Lab → real quadruped | Advanced | €1500+ | Domain randomisation, sim-to-real, reward shaping |\n| 12 | Digital twin: AAS + OPC UA of a real machine | Advanced | €0 | Information modelling — the Industry 4.0 skill that gets hired |\n| 13 | Design + build a QDD actuator, characterise it | Advanced | €300 | Torque density, backdrivability, thermal, transparency 🔬 |\n| 14 | UMI-style handheld data collection rig | Advanced | €200 | Robot-free data collection at scale 🔬 |\n\n**Project-based learning resources:**\n- 🧪 [Modern Robotics course projects](http://hades.mech.northwestern.edu/index.php/Modern_Robotics) — mobile manipulation capstone in CoppeliaSim\n- 🧪 [Articulated Robotics](https://articulatedrobotics.xyz/) 🆓 — the best step-by-step ROS 2 mobile robot build series on the internet ⭐\n- 🧪 [Robotics Backend](https://roboticsbackend.com/) 🆓/💵 — practical ROS 2 tutorials\n- 🧪 [James Bruton](https://www.youtube.com/@jamesbruton) 🆓 — mechanical-first robot builds; excellent for design intuition\n- 🧪 [Skyentific](https://www.youtube.com/@Skyentific) 🆓 — actuator and mechanism design deep-dives\n\n---\n\n## 10. Hardware You Can Actually Buy or Build\n\nOpen-source and low-cost platforms, roughly by price.\n\n| Platform | ~Cost | Type | Notes |\n|---|---|---|---|\n| [SO-101 / SO-ARM101](https://github.com/TheRobotStudio/SO-ARM100) | €120–350 | 6-DoF arm pair | 🆓 open hardware. Leader/follower teleoperation; the LeRobot reference platform. Kits from Hiwonder, Seeed, WowRobo. ⭐ |\n| [LeKiwi](https://github.com/SIGRobotics-UIUC/LeKiwi) | €400 | Mobile manipulator | SO-101 on a holonomic base; open source |\n| [Koch v1.1](https://github.com/jess-moss/koch-v1-1) | €250 | 5-DoF arm | The predecessor design; still a good build |\n| [ALOHA / ALOHA 2](https://aloha-2.github.io/) | €5k–20k | Bimanual | The reference bimanual teleoperation setup |\n| [Open Duck Mini](https://github.com/apirrone/Open_Duck_Mini) | €400 | Bipedal | Approachable legged-robot learning platform |\n| [Reachy 2 / Reachy Mini](https://www.pollen-robotics.com/) | €300–70k | Humanoid | Pollen Robotics (Hugging Face); open source |\n| [Unitree Go2 / G1](https://www.unitree.com/) | €1.6k–16k | Quadruped / humanoid | The default research legged platforms; SDK is usable |\n| [Franka Research 3](https://franka.de/) | €25k+ | 7-DoF arm | The academic manipulation standard; excellent torque control |\n| [UR cobots](https://www.universal-robots.com/) | €20k+ | Cobot | The industrial collaborative standard; good ROS 2 driver |\n| [TurtleBot 4](https://turtlebot.github.io/turtlebot4-user-manual/) | €1.5k | Mobile | The canonical ROS 2 teaching robot |\n| [Duckietown](https://duckietown.com/) | €300+ | Mobile / education | Complete autonomy curriculum in a box 🎓 |\n| [Open Dynamic Robot Initiative](https://open-dynamic-robot-initiative.github.io/) | €3k+ | Legged actuators | 🆓 Open QDD actuator + leg designs from MPI/NYU |\n\n---\n\n## 11. Classic Mechatronic Systems\n\nWorked examples worth studying, because each one is a complete mechatronic argument:\n\n- [**ABS**](https://en.wikipedia.org/wiki/Anti-lock_braking_system) — wheel-slip estimation from noisy sensors under hard real-time constraints. The canonical automotive mechatronic system.\n- [**3D printers**](https://en.wikipedia.org/wiki/3D_printing) — motion control, thermal control, and (in [Klipper](https://www.klipper3d.org/)) input shaping and pressure advance. An accessible, complete control-systems case study. 🧪\n- [**GNSS/INS**](https://en.wikipedia.org/wiki/Satellite_navigation) — sensor fusion; the practical home of the Kalman filter.\n- [**PLCs**](https://en.wikipedia.org/wiki/Programmable_logic_controller) — deterministic scan-cycle computation; a genuinely different computational model worth understanding.\n- [**Hybrid \u0026 electric drivetrains**](https://en.wikipedia.org/wiki/Hybrid_vehicle) — power-split control, energy management, thermal.\n- [**Hard disk drives**](https://en.wikipedia.org/wiki/Hard_disk_drive) — the highest-precision mass-produced servo system ever built; nanometre positioning at kHz bandwidth.\n- [**Washing machines**](https://en.wikipedia.org/wiki/Washing_machine) — unbalance detection, drum resonance avoidance, cost-driven sensor minimisation. Deceptively deep.\n- [**Surgical robots**](https://en.wikipedia.org/wiki/Robot-assisted_surgery) — teleoperation, force reflection, safety architecture.\n- [**Wafer steppers**](https://en.wikipedia.org/wiki/Stepper) — the extreme end: sub-nanometre stages, feed-forward everything, the field's hardest control problems.\n\n---\n\n## 12. Trends Radar 2026\n\nWhere things stand, honestly assessed.\n\n| Trend | Maturity | Why a mechatronic engineer should care |\n|---|---|---|\n| **VLAs / generalist robot policies** | Early production | Changes what \"programming a robot\" means. Hardware must now be *learnable*, not just controllable |\n| **Diffusion \u0026 flow-matching policies** | Production-ready | The strong default for contact-rich manipulation; robust to multimodal demonstrations |\n| **Real-time action chunking** | Production-ready | Made large policies runnable at real robot rates — the practical unlock of 2025 |\n| **GPU physics (Newton, MuJoCo-Warp)** | Rapidly maturing | Two orders of magnitude more simulation throughput; sim-first design becomes viable for small teams |\n| **World models (Cosmos, Genie)** | Research → early product | Synthetic data instead of more robots; expect commercial integration around 2027 |\n| **Agentic AI in plant operations** | Pilots, few deployments | Real value in exception handling and supervised execution; the safety architecture is the engineering problem |\n| **MCP as industrial glue** | Early, fast-moving | Becoming the standard way models reach OPC UA / MES / historians |\n| **Humanoids** | Overhyped, genuinely improving | Massive investment; the hard problems remain actuation, energy, hands and reliability — mechanical problems |\n| **Compliant \u0026 backdrivable actuation** | Mature, resurging | Learned policies need hardware that survives contact; QDD, SEA and VSA are back in demand |\n| **Morphological computation / metamaterials** | Research | Offload control effort into the structure; watch this over the next five years 🔬 |\n| **Digital twin standardisation (AAS)** | Mature standard, slow adoption | The Industry 4.0 skill with the best employability-to-effort ratio |\n| **Edge AI / TinyML on MCUs** | Mature | Condition monitoring and anomaly detection without cloud dependency |\n| **Digital Product Passport / ecomechatronics** | Regulatory-driven | EU rules will make this mandatory work, not optional differentiation |\n| **Embedded Rust** | Growing | Memory safety in firmware; adoption rising in safety-relevant contexts |\n| **Robot data scarcity** | *The* bottleneck | The 2026 consensus: progress is limited by data infrastructure, not model architecture |\n\n---\n\n## 13. Journals, Conferences, Communities\n\n**Journals**\n- [IEEE/ASME Transactions on Mechatronics](https://www.ieee-ims.org/publication/tmech) — the flagship\n- [Mechatronics (Elsevier)](https://www.sciencedirect.com/journal/mechatronics)\n- [IEEE Transactions on Robotics (T-RO)](https://www.ieee-ras.org/publications/t-ro)\n- [International Journal of Robotics Research (IJRR)](https://journals.sagepub.com/home/ijr)\n- [Science Robotics](https://www.science.org/journal/scirobotics)\n- [IEEE Robotics and Automation Letters (RA-L)](https://www.ieee-ras.org/publications/ra-l)\n- [IEEE Transactions on Industrial Electronics / Informatics](https://www.ieee-ies.org/pubs)\n- [Mechanism and Machine Theory](https://www.sciencedirect.com/journal/mechanism-and-machine-theory)\n- [Frontiers in Robotics and AI](https://www.frontiersin.org/journals/robotics-and-ai) 🆓 open access\n\n**Conferences**\n- [ICRA](https://www.ieee-ras.org/conferences-workshops/fully-sponsored/icra) · [IROS](https://www.ieee-ras.org/conferences-workshops/fully-sponsored/iros) — the two big robotics conferences\n- [RSS](https://roboticsconference.org/) — smaller, higher signal-to-noise; where Diffusion Policy and ACT appeared\n- [CoRL](https://www.corl.org/) — the robot learning conference\n- [AIM](https://www.ieee-ras.org/conferences-workshops/financially-co-sponsored/aim) — IEEE/ASME Advanced Intelligent Mechatronics\n- [Hannover Messe](https://www.hannovermesse.de/) · [SPS Nuremberg](https://sps.mesago.com/) · [automatica](https://automatica-munich.com/) — where industrial reality shows up\n- [NVIDIA GTC](https://www.nvidia.com/gtc/) — increasingly where robotics platform announcements land\n\n**Communities**\n- [ROS Discourse](https://discourse.ros.org/) · [Robotics Stack Exchange](https://robotics.stackexchange.com/)\n- [LeRobot](https://github.com/huggingface/lerobot) — very active Discord; the current invite is linked from the repo README\n- [r/robotics](https://reddit.com/r/robotics) · [r/PLC](https://reddit.com/r/PLC) (the best industrial-automation community online) · [r/ControlTheory](https://reddit.com/r/ControlTheory)\n- [Hugging Face Robotics](https://huggingface.co/robotics)\n\n---\n\n## 14. Related Awesome Lists\n\n**Robotics \u0026 learning**\n- [Awesome Robotics](https://github.com/ahundt/awesome-robotics) · [Awesome Robotics Libraries](https://github.com/jslee02/awesome-robotics-libraries)\n- [Awesome ROS2](https://github.com/fkromer/awesome-ros2)\n- [Awesome VLA](https://github.com/KwanWaiPang/Awesome-VLA) — actively maintained VLA paper tracker\n- [Awesome Physical AI](https://github.com/keon/awesome-physical-ai) — VLA, world models, embodied AI, robot foundation models\n- [Awesome LLM Robotics](https://github.com/GT-RIPL/Awesome-LLM-Robotics)\n- [Awesome Embodied AI](https://github.com/haoranD/Awesome-Embodied-AI)\n- [robotics-coursework](https://github.com/mithi/robotics-coursework) — where to learn robotics online\n- [PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics) — algorithms with readable code\n\n**Engineering \u0026 embedded**\n- [Awesome Embedded](https://github.com/nhivp/Awesome-Embedded) · [Awesome Embedded Rust](https://github.com/rust-embedded/awesome-embedded-rust)\n- [Awesome Embedded and IoT Security](https://github.com/fkie-cad/awesome-embedded-and-iot-security)\n- [Awesome Electronics](https://github.com/kitspace/awesome-electronics)\n- [Awesome Mechanical Engineering](https://github.com/m2n037/awesome-mecheng)\n- [Awesome TinyML](https://github.com/gigwegbe/tinyml-papers-and-projects)\n- [Awesome C++](https://github.com/fffaraz/awesome-cpp) · [Awesome Python](https://github.com/vinta/awesome-python)\n\n**AI**\n- [Awesome Computer Vision](https://github.com/jbhuang0604/awesome-computer-vision)\n- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) · [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)\n- [Awesome MCP Servers](https://github.com/modelcontextprotocol/servers)\n\n---\n\n## Contributing\n\nContributions are welcome — additions, corrections and dead-link reports alike.\n\nThe rules in short: prefer **free** and **primary** sources, mark paid ones with 💵, include the **year** for anything in the fast-moving sections, and give one line on *why* a resource is worth someone's time. Keep the mechatronic point of view — this is not a general AI, ME or EE list.\n\n**[Read CONTRIBUTING.md](CONTRIBUTING.md)** for the full guidelines, entry format and marker conventions.\n\n## Licence\n\n[![CC0](https://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)\n\nTo the extent possible under law, the contributors have waived all copyright and related rights to this work.\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/engyasin%2Fawesome-mechatronics/projects"}