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EPICLab-ManiSkill

This repository is the official submission of EPIC Lab for no external annotation track of SAPIEN ManiSkill Challenge 2021.

Dependency

Please see environment.yml, we build our method on top of ManiSkill-Learn.

Data

Please download ManiSkill demonstration dataset from here and store it in the folder training/data.

Training

The training code is provided in training.

OpenCabinetDoor: run the shell command training/scripts/train_rl_agent/run_GAIL_door.sh

OpenCabinetDrawer: run the shell command training/scripts/train_rl_agent/run_SAC_drawer.sh

PushChair: run the shell command training/scripts/train_rl_agent/run_GAIL_chair.sh

MoveBucket: run the shell command training/scripts/train_rl_agent/run_SAC_bucket.sh

Evaluation

The evaluation code and the submisstion checkpoints of four tasks are provided in evaluation. You can use evaluate_policy.py from ManiSkill to run the model:

PYTHONPATH=YOUR_SOLUTION_DIRECTORY:$PYTHONPATH python mani_skill/tools/evaluate_policy.py --env ENV_NAME

For example, on OpenCabinetDoor, to evaluate the model:

PYTHONPATH=evaluation/Door:$PYTHONPATH python evaluate_policy.py --env OpenCabinetDoor-v0

Trained models

Our trained models can be found at:

OpenCabinetDoor: Checkpoint

OpenCabinetDrawer: Checkpoint

PushChair: Checkpoint

MoveBucket: Checkpoint