Diffusion Policy (image, UNet) checkpoints trained with train.py of https://github.com/KuanchengWang/oopsie (branch dp-teleop-block) on Franka teleop data. Dataset teleopdataset20260922211038 (40 episodes, 15840 steps @ 10 Hz, all demos in training, no held-out validation). 3000-epoch run started 2026-09-23 (batch 64, ~244 steps/epoch); the run keeps only latest.ckpt, so snapshots are taken when it passes the listed epochs. Loss values are in-sample train loss. (epochs 500 / 1000 / 1500 were overwritten before a copy could be taken; epochs 2000 / 2500 / 3000 will be added as the run passes them.) swap-animal/config.yaml is the resolved training config. The checkpoint's…
Independent publisher
Kuancheng Wang
harrywang01
Models in Library3
Datasets in Library0
Models on Hugging Face14
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Published tasks: Robotics.
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Models
CAMP (Compressed Action Memory Policy) gives a visuomotor policy a behavioral memory. A recurrent memory is pretrained to reconstruct a compressed (DCT) summary of the robot's own past actions, so its hidden state has to encode what the robot already did; a Diffusion Policy is then conditioned on that state. This lets the policy track task progress and learn from its own failed attempts, which a memoryless policy cannot do when the current image does not determine the next action. This repository holds the CAMP policies for all nine tasks of RMBench, a memory-dependent bimanual manipulation benchmark built on RoboTwin 2.0 (Aloha-AgileX dual-arm robot). Every policy is trained from the…