bigfish_codes
The training script is in the root path, whose name is run_bc_chair/bucket/door/drawer.sh. Due to files storage limits, we put the trained models in the Google Drive(https://drive.google.com/file/d/1G9SGPXGF_KA3nISqVqG9ZOraobX9vWlj/view?usp=sharing). The evaluation script is the user_solution.py in the root path.
To make this repository clear, the differences with the official codebase(https://github.com/haosulab/ManiSkill-Learn) are introduced. Compared with the official codebase, I only change two hyperparameters in the bc_mani_skill_pointnet_transformer.py. Namely, num_heads and num_blocks. Besides, I trained the models with 1024 batch size and 1.2 million iterations and submitted the models that obtained the highest success rate on training set.
More specially,
- The best bucket model is 64 head, 12 block, which was submitted in December 29 and achieved the success rate 37.2 on the leaderboard.
- The best chair model is 64 head, 12 block, which was submitted in December 29 and achieved the success rate 23.2 on the leaderboard.
- The best drawer model is 64 head, 12 block, which was submitted in January 4 and achieved the success rate 49.6 on the leaderboard.
- The best door model is 16 head, 16 block, which was submitted in January 14 and achieved the success rate 41.6 on the leaderboard.
We plan to write a technical report for improving the future research.