{"id":19917971,"url":"https://github.com/danfeix/model-based-papers","last_synced_at":"2025-11-25T09:04:49.164Z","repository":{"id":81492248,"uuid":"161860566","full_name":"danfeiX/model-based-papers","owner":"danfeiX","description":"My reading list for model-based control","archived":false,"fork":false,"pushed_at":"2018-12-17T03:07:54.000Z","size":47,"stargazers_count":150,"open_issues_count":0,"forks_count":23,"subscribers_count":8,"default_branch":"master","last_synced_at":"2025-01-11T23:41:58.130Z","etag":null,"topics":["model-based-rl","reading-list"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/danfeiX.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2018-12-15T02:04:55.000Z","updated_at":"2024-11-20T20:30:02.000Z","dependencies_parsed_at":"2023-07-08T08:31:09.609Z","dependency_job_id":null,"html_url":"https://github.com/danfeiX/model-based-papers","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danfeiX%2Fmodel-based-papers","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danfeiX%2Fmodel-based-papers/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danfeiX%2Fmodel-based-papers/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danfeiX%2Fmodel-based-papers/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/danfeiX","download_url":"https://codeload.github.com/danfeiX/model-based-papers/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241348204,"owners_count":19948157,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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"}},"keywords":["model-based-rl","reading-list"],"created_at":"2024-11-12T21:52:02.759Z","updated_at":"2025-11-25T09:04:49.081Z","avatar_url":"https://github.com/danfeiX.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"A list of papers on model-based control that I have read so far. The ones that I particularly liked are marked with :star:.\n\n**Model Learning and Model-predictive Control (MPC)**\n- [Learning model-based planning from scratch](https://arxiv.org/abs/1707.06170), R. Pascanu and Y.Li et al., Arxiv 2017\n- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://arxiv.org/abs/1805.12114), K. Chua et al., NIPS 2018\n- [SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning](https://arxiv.org/abs/1808.09105), M. Zhang et al., arXiv 2018\n- [Interaction Networks for Learning about Objects, Relations and Physics](https://arxiv.org/abs/1612.00222), P. Battaglia et al., NIPS 2016 :star:\n- [Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids](https://arxiv.org/abs/1810.01566), Y. Li et al., arXiv 2018 :star:\n- [Propagation Networks for Model-Based Control Under Partial Observation](https://arxiv.org/abs/1809.11169), Y. Li et al., arXiv 2018\n- [Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation](https://arxiv.org/abs/1802.04325), D. Corneil et al., ICML 2018 :star:\n- [A Compositional Object-Based Approach to Learning Physical Dynamics](https://arxiv.org/abs/1612.00341), M. Chang et al., ICLR 2017 :star:\n- [SPNets: Differentiable Fluid Dynamics for Deep Neural Networks](https://arxiv.org/abs/1806.06094), C. Schenck et al., CoRL 2018\n- [Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing](https://arxiv.org/abs/1808.03246), A. Ajay et al., IROS 2018\n- [Graph networks as learnable physics engines for inference and control](https://arxiv.org/abs/1806.01242), A. Sanchez-Gonzalez et al., arXiv 2018 :star:\n- [Learning Latent Dynamics for Planning from Pixels](https://arxiv.org/abs/1811.04551), D. Hafner et al., arXiv 2018\n\n\n**Pixel to Control**\n- [Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images](https://arxiv.org/abs/1506.07365), M. Watter and J. Springenberg et al., NIPS 2015 :star:\n- [Robust Locally-Linear Controllable Embedding](https://arxiv.org/abs/1710.05373), E. Banijamali et al., AISTATS 2018\n- [End-to-End Training of Deep Visuomotor Policies](https://arxiv.org/abs/1504.00702), S. Levine et al., JMLR 2016 \n- [Unsupervised Learning for Physical Interaction through Video Prediction](https://arxiv.org/abs/1605.07157), C. Finn et al., NIPS 2016\n- [Deep Spatial Autoencoders for Visuomotor Learning](https://arxiv.org/abs/1509.06113), C. Finn et al., ICRA 2016\n- [Deep Visual Foresight for Planning Robot Motion](https://arxiv.org/abs/1610.00696), C. Finn et al., ICRA 2017\n- [Learning Plannable Representations with Causal InfoGAN](https://arxiv.org/abs/1807.09341), T. Kurutach et al., NIPS 2018 :star:\n- [Learning Latent Dynamics for Planning from Pixels](https://arxiv.org/abs/1811.04551), D. Hafner et al., arXiv 2018\n- [SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control](https://arxiv.org/abs/1710.00489), A. Byravan et al., ICRA 2018 :star:\n\n**Model-based + Model-free**\n- [MBMF: Model-Based Priors for Model-Free Reinforcement Learning](https://arxiv.org/abs/1709.03153), S. Bansal et al., CoRL 2017\n- [Continuous deep q-learning with model-based acceleration](https://arxiv.org/abs/1603.00748), S. Gu et al., ICML 2016\n- [Recurrent World Models Facilitate Policy Evolution](https://arxiv.org/abs/1809.01999), D. Ha et al., NIPS 2018\n\n**Learned Optimal Control**\n- [Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images](https://arxiv.org/abs/1506.07365), M. Watter and J. Springenberg et al., NIPS 2015 :star:\n- [Robust Locally-Linear Controllable Embedding](https://arxiv.org/abs/1710.05373), E. Banijamali et al., AISTATS 2018\n- [Differentiable MPC for End-to-end Planning and Control](https://arxiv.org/abs/1810.13400), B. Amos et al., NIPS 2018 :star:\n- [Path Integral Networks: End-to-End Differentiable Optimal Control](https://arxiv.org/abs/1706.09597), Okada et al., NIPS 2017\n- [Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning](http://www.roboticsproceedings.org/rss07/p08.pdf), M. Deisenroth et al., RSS 2011\n- [SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning](https://arxiv.org/abs/1808.09105), M. Zhang et al., arXiv 2018\n\n**State Estimation**\n- [Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data](https://arxiv.org/abs/1605.06432), M. Carl et al., ICLR 2017 :star:\n- [Deep Kalman Filters](https://arxiv.org/abs/1511.05121) R. G. Krishnan et al., arXiv 2015\n- [Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors](https://arxiv.org/abs/1805.11122), R. Jonschkowski et al., RSS 2018 :star:\n- [QMDP-Net: Deep Learning for Planning under Partial Observability](https://arxiv.org/abs/1703.06692), P. Karkus et al., NIPS 2017\n- [Generative Temporal Models with Spatial Memory for Partially Observed Environments](https://arxiv.org/abs/1804.09401), M. Fraccaro et al., ICML 2018 :star:\n\n**Survey**\n- [Learning Physical Dynamical Systems for Prediction and Control: A Survey](https://www.cs.princeton.edu/courses/archive/spring18/cos598B/public/projects/LiteratureReview/COS598B_spr2018_PhysicalDynamicalSystems.pdf), J. LaChance, 2018\n\n**Koopman Theory**\n- [Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition](https://arxiv.org/abs/1710.04340), N Takeishi et al., NIPS 2017 :star:\n- [Deep Dynamical Modeling and Control of Unsteady Fluid Flows](https://arxiv.org/abs/1805.07472), J. Morton et al., NIPS 2018 \n- [Deep learning for universal linear embeddings of nonlinear dynamics](https://arxiv.org/abs/1712.09707), B Lusch et al., Nature Communications 2018\n- [Data-driven discovery of Koopman eigenfunctions for control](https://arxiv.org/abs/1707.01146), E. Kaiser et al., arXiv 2017\n\n**Optimal Control**\n- [Control-Limited Differential Dynamic Programming](https://homes.cs.washington.edu/~todorov/papers/TassaICRA14.pdf), Y. Tassa et al., ICRA 2014\n\n**Other Resources**\n- [Learning Dynamical System Models from Data](http://rll.berkeley.edu/deeprlcoursesp17/docs/week_3_lecture_1_dynamics_learning.pdf), Sergey Levine, CS 294-112: Deep Reinforcement Learning\n- [EE263: Introduction to Linear Dynamical Systems](http://ee263.stanford.edu/lectures.html)\n- [CS 287: Advanced Robotics](https://people.eecs.berkeley.edu/~pabbeel/cs287-fa15/)\n- [Control Bootcamp](https://www.youtube.com/watch?v=Pi7l8mMjYVE\u0026list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m), Steve Brunton, 2017\n- [STUDYWOLF blog](https://studywolf.wordpress.com/), Travis DeWolf\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanfeix%2Fmodel-based-papers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanfeix%2Fmodel-based-papers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanfeix%2Fmodel-based-papers/lists"}