Codebase accompanying the paper OGMP: Oracle Guided Multimodal Policies for Agile and Versatile Robot Control. For support please raise an issue here or contact the authors.

Make a virtual environment,

python3 -m venv ogmp_env
source ogmp_env/bin/activate

Clone the repo,

git clone --depth 1 https://github.com/DRCL-USC/ogmp

To install the dependencies, run:

pip3 install -r requirements.txt

Install old version of torch (new may work but is not tested)

pip3 install torch==1.13.0+cpu --extra-index-url https://download.pytorch.org/whl/cpu

tested in python 3.8.10

To test the policy in paper, for each task, run

# for best parkour policy
python3 test.py --tstng_conf_path ./exp_confs/parkour_test.yaml --render_onscreen

# for best dive policy
python3 test.py --tstng_conf_path ./exp_confs/dive_test.yaml --render_onscreen

Similarly to train the best policy from the paper, run

# for parkour
python3 train.py --exp_conf_path ./exp_confs/parkour.yaml --recurrent --logdir ./logs/

# for dive
python3 train.py --exp_conf_path ./exp_confs/dive.yaml --recurrent --logdir ./logs/

To run the analyses from the paper, run

# for the sample mean of agility metrics and in-domain paramters
python3 analysis/n_rollout_test.py 

# for LMSR on flat ground
python3 analysis/flat_ground_lmsr_test.py 

# for LMSR at transition
python3 analysis/transition_lmsr_test.py 

The corresponding config file for each analyisis is in ./exp_confs/ folder. All the analyses files deploy multiprocessing, where each process will have a copy of a lstm policy and hence could be intensive. Set nop as per your system's capability to minimize time (default is 3 tested in a 32 Core, 64 GB RAM machine). Resulting plots and logs will be saved in ./results/<experiment_name>/<variant_id>.

• algos: contains the custom ppo implementation from link
• analysis: contains the code for analysis presenteed in the paper
• nn: contains the custom torch neural network (FF and LSTM), policy, critic implementation from link
• logs: contains the training logs, policies and encoders.
• dtsd: contains the environments
• exp_confs: contains the experiment configuration files for training and testing.
• train.py: file to train policies.
• test.py: file to test policies.

If you find this code useful, consider citing:

    @misc{krishna2024ogmp,
      title={OGMP: Oracle Guided Multimodal Policies for Agile and Versatile Robot Control}, 
      author={Lokesh Krishna and Nikhil Sobanbabu and Quan Nguyen},
      year={2024},
      eprint={2403.04205},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
    }