| --- |
| 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](https://github.com/knightnemo/VAM_Learn_from_Human_Video) |
| (branch `feat/efficient-ft`). |
|
|
| Each checkpoint is a diffusion policy fine-tuned with the **official |
| [irom-lab/dppo](https://github.com/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](https://github.com/ajwagen/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: |
|
|
| ```bash |
| export DPPO_EXPERT_ROOT=/path/to/experts # <task>/expert.pt layout |
| python -m algorithm.trainers.main algorithm=grpo_robomimic_can_expert |
| ``` |
|
|
