Bigenlight/diffusion_banana_in_pot_joint_fp16

Diffusion Policy — banana-in-pot (JOINT, fp16)

Diffusion Policy trained on the "put the right banana in the pot" task (UR7e + GELLO teleoperation, 2 RGB cameras), in JOINT action space (6 joints + gripper), using fp16 mixed-precision training.

• Checkpoint: step 80,000 (best open-loop MAE)
• Base library: LeRobot 0.6.1 (pin 8a74e0a)
• Dataset: Bigenlight/banana_in_pot_lerobot_v3 — 51 episodes / 21,524 frames / 30 fps
• fp32 sibling: Bigenlight/diffusion_banana_in_pot_joint

Architecture

ResNet18 (ImageNet-pretrained, trained end-to-end) + SpatialSoftmax vision encoder → FiLM-conditioned 1D temporal U-Net denoiser (DDPM-100, epsilon prediction). ~278M params. Images resized to 360×640, crop off. n_obs_steps=2, horizon=64, n_action_steps=32.

Training

• Precision: fp16 via HF Accelerate mixed_precision=fp16 (automatic GradScaler).
• Requires a dtype field on DiffusionConfig (absent upstream at this pin); launched with --policy.dtype=float16.
• Batch 8, 80k steps, AdamW, seed 1000, 45 train / 6 held-out episodes.
• Hardware: single RTX A4000. ~4.48 step/s, ~4.96 h (vs ~3.49 step/s / ~6.4 h fp32 → ~1.25× faster, ~22% less wall-clock). No NaN/instability.

Open-loop evaluation (DDIM-10, held-out episodes 45–50)

	fp32 @80k	fp16 @80k
poseMAE (rad)	0.0845	0.08268
gripper accuracy	0.953	0.960

fp16 matches (marginally beats, within noise) fp32 accuracy at ~22% lower training cost. Select the deploy checkpoint by open-loop MAE, not eval_loss (which rises during training for diffusion policies without indicating overfitting).

Intended use & limitations

Research artifact. Small single-task, single-scene, real-world (noisy) dataset of 51 success-only demonstrations; offline metrics only — no closed-loop hardware success rate measured yet. Not safety-validated for autonomous operation.