| --- |
| 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 |
| |
