https://github.com/aravindr93/trajopt

Trajectory optimization algorithms for robotic control.
https://github.com/aravindr93/trajopt

mujoco robotics robotics-control trajectory-optimization

Last synced: 3 months ago
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Trajectory optimization algorithms for robotic control.

• Host: GitHub
• URL: https://github.com/aravindr93/trajopt
• Owner: aravindr93
• License: mit
• Created: 2018-10-24T21:09:57.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-11-20T10:39:40.000Z (about 2 years ago)
• Last Synced: 2024-10-23T08:54:20.438Z (3 months ago)
• Topics: mujoco, robotics, robotics-control, trajectory-optimization
• Language: Python
• Homepage:
• Size: 51.8 KB
• Stars: 101
• Watchers: 7
• Forks: 17
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Trajectory Optimization Algorithms

This package contains trajectory optimization algorithms meant predominantly for continuous control taks (simulated with [MuJoCo](http://www.mujoco.org/)).

# Installation

The main package dependencies are `MuJoCo` and `mjrl`. See [setup-instructions](https://github.com/aravindr93/mjrl/tree/master/setup) to get a working conda environment and setup dependencies.

After [`mujoco_py`](https://github.com/openai/mujoco-py) has been installed, the package can be used by either adding to path as:

```

export PYTHONPATH=$PYTHONPATH

```

or through the pip install module

```

$ cd trajopt

$ pip install -e .

```

The tricky part of the installation is likely to be `mujoco_py`. Please see [instructions and known issues](https://github.com/aravindr93/mjrl/tree/master/setup) for help.

# API and example usage

The algorithms assume an environment abstraction similar to OpenAI `gym`, but requires two additional functions to be able to run the algorithms provided here.

- `get_env_state()` should return a dictionary with all the information required to reconstruct the scene and dynamics. For most use cases, this can just be the `qpos` and `qvel`. However, in some cases, additional information may be required to construct scene and dynamics. For example, in multi-goal RL, we can represent virtual goals using sites as opposed to real joints.

- `set_env_state(state_dict)` should take in a dictionary, and use the contents of the dictionary to recreate the scene specified by the dictionary.

The example [reacher environment](https://github.com/aravindr93/trajopt/blob/redesign/trajopt/envs/reacher_env.py) has an illustrative.

# Example Usage

See this directory for illustrative examples: [`trajopt/examples`](https://github.com/aravindr93/trajopt/tree/master/examples).

# Bibliography

If you find the package useful, please cite the following paper.

```

@INPROCEEDINGS{Lowrey-ICLR-19,

    AUTHOR    = {Kendall Lowrey AND Aravind Rajeswaran AND Sham Kakade AND 

                 Emanuel Todorov AND Igor Mordatch},

    TITLE     = "{Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control}",

    BOOKTITLE = {ICLR},

    YEAR      = {2019},

}

```