license: mit
tags:
- robotics
- robomimic
- diffusion-policy
- dppo
- reinforcement-learning
DPPO fine-tuned experts for robomimic (lift / can / square)
Teacher checkpoints used by the FlowDagger / DS-GRPO noise-space adaptation
experiments in VAM_Learn_from_Human_Video
(branch feat/efficient-ft).
Each checkpoint is a diffusion policy fine-tuned with the official
irom-lab/dppo recipe
(cfg/robomimic/finetune/<task>/ft_ppo_diffusion_mlp.yaml: DDPM 20 steps,
ft_denoising_steps=10, state observations, seed 42) starting from the
DPPO-released pre-trained (BC) checkpoints — DPPO does not release the
fine-tuned models, so these were trained from scratch on CMU Babel.
| task | file | arch (DiffusionMLP) | ft iters | success rate* |
|---|---|---|---|---|
| lift | lift/expert.pt |
512^3, time_dim 16 | state_80 | 99.5% |
| can | can/expert.pt |
512^3, time_dim 16 | state_150 | 100% |
| square | square/expert.pt |
1024^3 + cond_mlp[512,64], time_dim 32 | state_200 | 97.5% |
*200 stochastic episodes, sparse-success criterion, robomimic env stack from the DSRL fork (robosuite v1.4.1).
Format & usage
state_*.pt from DPPO fine-tuning: checkpoint["model"] holds actor.*
(base) and actor_ft.* (fine-tuned head; use ft_denoising_steps=10 denoise
routing). In the companion repo:
export DPPO_EXPERT_ROOT=/path/to/experts # <task>/expert.pt layout
python -m algorithm.trainers.main algorithm=grpo_robomimic_can_expert