Awesome

Introduction

This repository is the official implementation of Silver-Bullet-3D Solution for SAPIEN ManiSkill Challenge 2021

Requirement:

• PyTorch 1.8.0+
• Python3.7
• CUDA 10.1+

Other requirements please refer to environment.yml

Clone the repository:

git clone https://github.com/caiqi/Silver-Bullet-3D

No Interaction Track

Data preparation

• Download ManiSkill dataset from here
• Compress the data with compress_data.sh

Training

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

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.

No Restriction Track

The training and evaluation code is the same.

Acknowledgements

Thanks the contribution of ManiSkill-Learn and awesome PyTorch team.