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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
```
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