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Risk Conditioned Neural Motion Planning

Code of paper Risk Conditioned Neural Motion Planning

Citation

@inproceedings{huang2021risksac,
  title={Risk Conditioned Neural Motion Planning},
  author={Huang, Xin and Feng, Meng and Jasour, Ashkan and Rosman, Guy and Williams, Brian C},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2021},
  organization={IEEE}
}

Installation

  • Install the Conda environment: follow instructions for Miniconda from the website.

  • Install Conda environment.

cd <repo-location>
conda env create -f env.yml
conda activate risk_sac
pip install -e .

NOTE: If conda activate does not work, replace with source activate.

  • Add the repo directory to PYTHONPATH.

RL Agent Implementation

Modify in RLKit submodule.

Pull submodule after cloning this repo

git submodule add [email protected]:cyrushx/rlkit.git external/rlkit
git submodule update --init --recursive
git submodule update --remote --merge

Install rlkit library

cd external/rlkit
pip install -e .

Additional Comments:

Mujoco_py is not required, if see mujoco_py error, comment out following lines in risk_deeprl/external/rlkit/rlkit/envs/wrappers/init.py

from rlkit.envs.wrappers.image_mujoco_env import ImageMujocoEnv
from rlkit.envs.wrappers.image_mujoco_env_with_obs import ImageMujocoWithObsEnv

Train a standard SAC model in simple maze

Train model

python model/train_sac.py

Visualize training stats

python model/plot_learning_stats.py -i external/rlkit/data/YOUR_MODEL_PATH/

Train a risk-bounded model in simple maze

Train a model with fixed upper risk bound of 0.2

python model/train_risk_sac.py

Visualize training stats

python utils/plot_learning_stats.py -i external/rlkit/data/YOUR_MODEL_PATH/

Train risk-conditioned model in simple maze

Train a model

python model/train_risk_conditioned_sac.py --delta 0.2 --risk-coeff 10

Visualize stats

python utils/plot_learning_stats.py -i external/rlkit/data/YOUR_MODEL_PATH/

Visualize paths with baseline paths

python external/rlkit/scripts/run_policy.py external/rlkit/data/YOUR_MODEL_PATH/params.pkl -v --baseline

Train risk-conditioned model in FlyTrapBig maze

Train model

python model/train_risk_conditioned_sac.py --env FlyTrapBig --risk-coeff 20 --epochs 1200

Visualize paths with different risk bounds at a fixed starting location

python external/rlkit/scripts/run_policy.py external/rlkit/data/YOUR_MODEL_PATH/params.pkl -v

Visualize paths with the same risk bound at different starting locations

python external/rlkit/scripts/run_policy.py external/rlkit/data/YOUR_MODEL_PATH/params.pkl -v --multiple-start

Train risk-conditioned model with nonlinear Dubins dynamics

Train model

python model/train_risk_conditioned_sac.py --env TwoRooms --delta 0.1 --risk-coeff 20 --epochs 500 --dubins

Visualize paths

python external/rlkit/scripts/run_policy.py external/rlkit/data/YOUR_MODEL_PATH/params.pkl -v