---
license: mit
library_name: pytorch
pipeline_tag: robotics
tags:
  - robotics
  - imitation-learning
  - diffusion-policy
  - r1lite
datasets:
  - at237299966/r1lite_insert_flower_single
---

# Diffusion Policy (UNet + DDPM) — R1 Lite "insert flower"

Baseline checkpoints for the ODE-policy comparison on the Galaxea R1 Lite right arm. This is the original
**Diffusion Policy** recipe (Chi et al., 2023): a 1-D conditional UNet predicts the noise of a 16-step
action window (8 past + 8 future) under a DDPM schedule, conditioned on 8 frames of two cameras and
proprioception. Trained with exactly the same data, encoder, batch, learning rate and schedule as the ODE
policy ([`at237299966/ODE_policy_insert_flowers`](https://huggingface.co/at237299966/ODE_policy_insert_flowers))
and the flow-matching baseline ([`at237299966/fm_dit_insert_flowers`](https://huggingface.co/at237299966/fm_dit_insert_flowers)).

## Files

| Path | Content |
|---|---|
| `checkpoints/{50,100,150,200,250,300}.ckpt` | Full training checkpoints (755 MB each): `model`, `ema_model`, `optimizer` state dicts + the pickled Hydra config. Serve the EMA weights (the loader's default). |
| `config/config.yaml`, `config/overrides.yaml` | Resolved training config and the command-line overrides of this run |
| `logs/logs.json.txt` | Per-step / per-epoch training log (loss, lr, epoch time, val_loss every 5 epochs) |
| `SHA256SUMS` | Checksums of every uploaded file |

Checkpoints are `torch.save(..., pickle_module=dill)` payloads written by the ODE_Policy training runner;
loading needs that codebase (`roboverse_learn.il.policies.dp.ddpm_unet_image_policy.DiffusionUnetImagePolicy`)
and `torch.load(..., pickle_module=dill, weights_only=False)` — only load checkpoints you trust.

## Data

[`at237299966/r1lite_insert_flower_single`](https://huggingface.co/datasets/at237299966/r1lite_insert_flower_single):
LeRobot v2.1, 95 episodes / 23,034 frames at 15 Hz, single task "insert flower".

| Input / output | Definition |
|---|---|
| Cameras | `head_rgb` (720×1280) and `right_wrist_rgb` (360×640), **padded to square** then resized to 256×256 (same geometry as the robot client) |
| `agent_pos` | right-arm joint positions (6, rad) + right gripper opening (1, mm) |
| Action | right-arm **absolute joint targets** (6, rad) + gripper command (1, mm; 1 = closed, 100 = open), as recorded |
| Window | 8 observation frames; the policy predicts 16 steps and rows 7..14 (the current step and the next 7) are the action chunk |

## Training

| | |
|---|---|
| Model | ResNet18 (GroupNorm, random crop) per camera + proprioception, 8 frames → global condition (FiLM); ConditionalUnet1D with `down_dims=[48, 96, 192]`, kernel 5 (24.80M parameters outside the 22.35M encoder) |
| Objective | DDPM, 100 train timesteps, squared-cosine schedule, ε-prediction, sample clipping; 100 denoising steps at inference |
| Optimisation | AdamW, lr 1.5e-4, cosine schedule over 300 epochs, warm-up 500 steps, EMA |
| Batch | 2 × A100-40GB (DDP), 48 per GPU = 96 global; 236 steps/epoch, 70,800 steps in total |
| Wall clock | 9 h 12 min (110 s/epoch) |

| epoch | 0 | 10 | 50 | 100 | 150 | 200 | 250 | 300 |
|---|---|---|---|---|---|---|---|---|
| train loss | 0.82 | 0.034 | 0.013 | 0.0071 | 0.0048 | 0.0026 | 0.0015 | 0.0012 |
| val loss | 0.54 | 0.037 | 0.022 | 0.029 | 0.046 | 0.046 | 0.118 | 0.068 (ep 295) |

The validation loss (one held-out episode, raw weights) is lowest at epoch 40 (0.0185) and rises afterwards,
so compare early and late checkpoints on the robot. Loss values are not comparable across methods.

## Deployment

Same server and wire protocol as the ODE policy; the robot side is the unmodified openpi-r1lite closed-loop
client (15 Hz, executes row 0 of each `(8, 14)` reply):

```bash
python -m roboverse_learn.il.deploy.serve_policy \
    --checkpoint checkpoints/100.ckpt --host 0.0.0.0 --port 8000 --device cuda:0 --replan_every 8
```

**Latency caveat:** with the 100-step DDPM sampler one prediction takes ~667 ms on an A100, ten times the
66.7 ms tick of the 15 Hz loop, so the robot would stall on every re-planning tick. Real-time use needs a
faster sampler on the same weights (e.g. DDIM with ~10–16 steps, as in the original Diffusion Policy
deployment); the server does not offer one yet. Code: `ODE_Policy` repository, `neural_ode/real/` and
`roboverse_learn/il/deploy/`.