license: mit
library_name: lerobot
pipeline_tag: robotics
tags:
- robotics
- imitation-learning
- diffusion-policy
- lerobot
- pusht
- sample-efficiency
Diffusion Policy · PushT · sample-efficiency study
Four Diffusion Policy checkpoints for the PushT manipulation task (LeRobot / gym-pusht),
from a controlled study: under a small budget of demonstrations, does aggressive regularization
buy sample-efficiency for imitation-learning policies?
Trained & evaluated on AMD ROCm (Radeon AI PRO R9700, gfx1201).
Checkpoints
Each folder holds the final 150k-step policy (model.safetensors + config + pre/post-processors),
loadable with LeRobot.
| Folder | Recipe | Demos | Success (pc_success, 100 eval eps) |
|---|---|---|---|
standard-200demos |
standard | 200 | 29% |
enhanced-200demos |
enhanced | 200 | 29% |
standard-100demos |
standard | 100 | 8% |
enhanced-100demos |
enhanced | 100 | 19% |
- standard = LeRobot defaults.
- enhanced = aggressive regularization: weight decay
1e-3+ image-transform data augmentation.
Result
| Demos | Standard | Enhanced | Δ |
|---|---|---|---|
| 200 (data-rich) | 29% | 29% | 0 pts |
| 100 (data-scarce) | 8% | 19% | +11 pts |
Preliminary evidence suggests that, under a 100-demo budget, enhanced regularization improves PushT success from 8% to 19% (+11 pts), while showing no gain at 200 demos (29% = 29%). This is consistent with regularization helping most when demonstrations are scarce (inspired by Konwoo et al. on data-constrained pre-training). Single seed — a strong signal, not yet a settled claim.
Training
- Policy: Diffusion Policy · Task: PushT (sim)
- 150,000 steps, seed 0, eval on 100 episodes
- Hardware: AMD Radeon AI PRO R9700 (ROCm), ~11 steps/s
results/contains the raw eval CSVs (sweep_b*.csv).
Limitations
- Single seed (0) — deltas are a signal, not yet a claim; multiple seeds + error bars pending.
- Baseline below the published reference (~65%) — the remaining gap is batch size (8 vs ~64); addressable with gradient accumulation.
- Preliminary: enough to show the effect, not yet to publish.
Links
- Code & write-up: https://github.com/enyolanev-bit/sample-efficient-imitation
- Case study: https://enyolanev-bit.github.io/sample-efficient-imitation/
- Interactive results: https://enyolanev-bit-sample-efficient-imitation.static.hf.space