dp-blockpick-10k-table-texture-statenoise

LeRobot diffusion policy (ROBOTIS lerobot-cyclo fork, 288 M parameters) for a top-down block pick with the left arm of the ROBOTIS FFW SG2 (SG2 + ROBOTIS CAD gripper, calibrated wrist cameras, 29.25 in table).

Files at the repo root = step 100k (DiffusionPolicy.from_pretrained("padiredd/dp-blockpick-10k-table-texture-statenoise")); step-10000/, step-20000/, step-30000/, step-40000/, step-50000/, step-60000/, step-70000/, step-80000/, step-90000/ = intermediate checkpoints (every 10k steps).

data	padiredd/pick-cube-topdown-region10k-dr-dr2-3cam: 10,000 sim demos, 1,695,003 frames, same task, region and dr2 randomization as region10k-full (start pose +-2 deg, gripper open/close per episode, slow pushes with clean labels, spotlights, camera shifts, demo timing, table height -2..+2 cm) plus appearance randomization: backdrop (real lab photo / ambientCG texture / grid), table look (nominal / flat colour / texture), block colour + roughness
sampling	uniform over all frames (LeRobot default EpisodeAwareSampler)
inputs / outputs	head + left wrist + right wrist cameras, each 240x240, + 22-dim joint state; action = 16 joint targets, 15 Hz
image augmentation	heavy, random order, up to 5 of: brightness 0.6-1.4, contrast 0.6-1.4, saturation 0.3-1.7, hue +-0.1, sharpness 0.3-2.0, affine +-10 deg / 10 % / 0.9-1.1, perspective 0.2, blur, random erasing, grayscale
state noise	1 deg Gaussian noise on the 7 left-arm joint positions of the state input during training (state_noise_std in config.json is a field of our lerobot-cyclo patch; delete it to load with stock lerobot-cyclo, it is training-only)
recipe	100k steps, batch 32, ResNet18 per camera, Adam 1e-4, cosine, DDPM 100 steps, horizon 64 / 32 executed, n_obs_steps 1, seed 1000
hardware	one NVIDIA RTX 4090 24 GB (lab225), 10.4 steps/s

Task string: "pick up the lime green block with the left arm". Evaluation results are added once they are run.