harrywang01/CAMP-RMBench-Checkpoints

CAMP on RMBench

Policies for the paper "Remember what you did: learning behavioral memory for robot manipulation" (CAMP, code: https://github.com/ucsdarclab/CAMP), trained on the RMBench (RoboTwin 2.0, Aloha-AgileX) benchmark from the 50 released demonstrations per task. Evaluated with RMBench's own protocol (demo_clean, seeds from 100000 validated by the scripted expert, per-task step limits, 100 episodes).

task	success (100 episodes)	folder
rearrange_blocks	100 / 100	rearrange_blocks/
blocks_ranking_try	100 / 100	blocks_ranking_try/
put_back_block	100 / 100	put_back_block/
battery_try	97 / 100	battery_try/

Files

Each task folder holds only inference weights (no optimizer, scheduler or training bookkeeping):

• policy.ckpt — CAMP policy (Diffusion Policy conditioned on the compressed action memory), EMA weights and the resolved training config.
• memory/best_model.pt — the Stage-1 action-memory LSTM (weights + architecture args) the policy was trained with.
• memory/normalizer.pt — its input normaliser.

Recipe (all tasks)

• Stage 1: memory LSTM pretrained on the 50 demos to reconstruct its past actions (DCT heads), head camera 96x128 + 14-D joint state, hidden 128, action subsampling 4.
• Stage 2: Diffusion Policy (head camera 240x320, 14-D joint targets, n_obs_steps=1, 8-step action chunks) conditioned on the memory through a 32-D projection; memory frozen for 400 epochs, then jointly finetuned (200 epochs; put_back_block 600). The checkpoint reported per task is the best one over evaluated epochs.

Usage

# inside the CAMP + RoboTwin evaluation image (see scripts/rmbench/eval in the CAMP repo)
python scripts/rmbench/eval/rmbench_eval.py eval --task rearrange_blocks --ckpt policy --episodes 100 \
    --ckpt_root <this repo>/stage2 --stage1_root <this repo>/stage1

where stage2/<task>/checkpoints/policy.ckpt and stage1/<task>/{best_model.pt,normalizer.pt} point at the files of this repo (symlink or copy). The policy adapter is scripts/rmbench/eval/policy_CAMP and follows RMBench's get_model / eval / reset_model interface.

Citation

@article{wang2026rememberdidlearningbehavioral,
  title   = {Remember What You Did: Learning Behavioral Memory for Robot Manipulation},
  author  = {Wang, Kuancheng and Yeom, Hyunsoo and Cao, Yifan and Zhi, Huanyu and Shinde, Ishan and Yip, Michael C.},
  journal = {arXiv preprint arXiv:2606.21188},
  year    = {2026}
}