megayeye / scdm

Solving Complex Dexterous Manipulation Tasks with Trajectory Optimisation and Reinforcement Learning

Solving Complex Dexterous Manipulation Tasks with Trajectory Optimisation and Reinforcement Learning

This repository contains code for our ICML 2021 paper: Solving Complex Dexterous Manipulation Tasks with Trajectory Optimisation and Reinforcement Learning (link to arXiv version). Videos showcasing the obtained results can be found on the main project page. Requirements:

• Mujoco-py
• Pytorch
• Numpy
• joblib
• Dexterous-Gym

Install with pip install -e .

TOPDM contains the code for the trajectory optimisation algorithm. See SCDM/TOPDM/example_experiments.sh for examples of how to run this. Note that this cleaned version of the code seems to be running more slowly than an earlier version - currently looking into this.

TD3_plus_demos contains the code for combining demonstrations with reinforcement learning for the PenSpin task. See SCDM/TD3_plus_demos/run_experiment.sh to run.

We also provide prerun trajectories for all of the environments in SCDM/TOPDM/prerun_trajectories, as well as a file to render these (SCDM/TOPDM/prerun_trajectories/render_demonstrations.py)

We later on also added a version of TOPDM applied to the Humanoid-v3 environment in OpenAI's gym. This is contained in SCDM/TOPDM/humanoid_experiments.