This repository is the official implementation of Silver-Bullet-3D Solution for SAPIEN ManiSkill Challenge 2021
- PyTorch 1.8.0+
- Python3.7
- CUDA 10.1+
Other requirements please refer to environment.yml
git clone https://github.com/caiqi/Silver-Bullet-3D
- Download ManiSkill dataset from here
- Compress the data with compress_data.sh
Training code is provided in No_Interaction/training folder. For example, to train MoveBucket model, using following script:
CONFIG_NAME=bucket/1225_bucket_ensemble_v1
SEED=1345
CUDA_VISIBLE_DEVICES=0 python -m tools.run_rl configs/${CONFIG_NAME}.py --gpu-ids=0 --seed ${SEED} --work-dir ${CONFIG_NAME}
For final submission, we ensemble multiple models with different network architecture and random seed. All configs are provided in configs folder.
Evaluation code and checkpoints are provided in No_Interaction/evaluation. For example, to evaluate the pre-trained models on MoveBucket, use ManiSkill official evaluation code:
PYTHONPATH=No_Interaction/evaluation/bucket_track1:$PYTHONPATH python evaluate_policy.py --env MoveBucket-v0 --level-range "0-300"
evaluate_policy.py is from ManiSkill repo. Checkpoints can be downloaded from release page.
Task | Models |
---|---|
OpenCabinetDoor | Checkpoint |
OpenCabinetDrawer | Checkpoint |
MoveBucket | Checkpoint |
PushChair | Checkpoint |
The training and evaluation code is the same.
Task | Code |
---|---|
OpenCabinetDoor | user_solution_door.py |
OpenCabinetDrawer | user_solution_drawer.py |
MoveBucket | user_solution_bucket.py |
PushChair | user_solution_chair.py |
Thanks the contribution of ManiSkill-Learn and awesome PyTorch team.