Dimios45/yam-pick-duster-200-bspline-ee

YAM Pick-Duster 200 — B-spline Diffusion Policy (end-effector)

A B-spline Policy UNet diffusion policy trained on `Dimios45/yam-pick-duster-200-ee`: 200 teleop demos of an I2RT YAM arm picking a blue duster and placing it in a red box.

This model emits absolute Cartesian poses and requires IK on the robot. The joint-space counterpart — same 200 takes, no IK, and the one recommended for deployment — is `Dimios45/yam-pick-duster-200-bspline-joint`. The pair exists to compare action spaces on identical demonstrations.

Read this before deploying. The dataset card states these poses are MuJoCo FK and disagree with bspline-policy's pyroki/URDF by up to ~9 mm, and advises: "do not deploy a policy trained on this data through its Cartesian path without resolving the discrepancy — drive the arm in joint space instead." Their suggested mitigations are EEFollower with low mu, target_frame="tcp", and reach clamping.

Supersedes the 50-episode `yam-pick-duster-bspline-ee`.

Action and observation space

raw actions (7,) = [pos(3), rotvec(3), gripper(1)]
   -> dataset expands to (10,) = [pos(3), rot6d(6), gripper(1)]
gripper: 0 = open, 1 = closed

rotation_rep: rotation_6d with abs_action: True. infer_action_meta resolves this to `single_yam_rot6d`. Network output is (16, 11): column 0 is the knot vector in units of 10 Hz frames, columns 1-10 are control points.

TCP frame is the link_6 flange with a fixed 90 degree z-rotation, ~13.5 cm from the fingertip.

Files

Training

Loss: 0.408 (ep0) -> 0.015 (50) -> 0.011 (100) -> 0.008 (150) -> 0.006 (200) -> 0.005 (250) -> 0.003 (300-350) -> 0.002 (400), final in-epoch 0.00157.

The 401-epoch budget was chosen on gradient-step count (~190k steps), not copied from the 50-episode run. checkpoint_every: 50 with 401 (not 400) epochs so the fully-annealed final epoch is actually written.

These numbers are not comparable to the joint-space model's: different action space, units, and normalizer. Only the shape of each curve is meaningful on its own.

Inference latency

Measured on an idle GPU, batch 1, two 84x84 cameras:

A chunk spans ~1.1 s of wall time at 10 Hz, so all settings clear comfortably even on a NUC-class CPU (expect 2-3x the CPU column). 16 steps is a safe default here.

Rollout

cd ~/bspline-policy
export PYTHONPATH=$PWD/bspline_policy:$PWD/diffusion_policy:$PWD/real_env/yam_teleop

hf download Dimios45/yam-pick-duster-200-bspline-ee deploy_ema.ckpt --local-dir ./ckpt

# terminal 1 - arm server
sudo ip link set can_follower_r up type can bitrate 1000000
python real_env/yam_teleop/yam_server.py --channel can_follower_r

# terminal 2 - rollout
python real_env/yam_teleop/rollout_local_policy.py \
  --env yam-vrkit --policy bspline \
  --ckpt-path ./ckpt/deploy_ema.ckpt \
  --diffusion-policy-dir $PWD/diffusion_policy \
  --control-freq 100 \
  --data-freq 10 \
  --origin-time-scale 10 \
  --num-inference-steps 16 \
  --predict-before-end 0.3 \
  --speed-up-times 1.0 \
  --save --output-dir data/rollouts_ee_200

`--env yam-vrkit` is not in upstream bspline-policy. rollout_local_policy.py accepts only x5, tidybot2, and yam, so this fails at argparse until a matching env class is added. Substituting --env yam routes through RealEnv + yam_server's pyroki velocity IK — which is exactly the Cartesian path the dataset card warns about above.

Flags that are not optional

A missing top_image key does not raise: policy_local_bspline.py:625-629 substitutes a black frame, so an unwired or dead camera yields a half-blind policy rather than an error.

Cameras, gripper, data notes

Trained uncropped (RAW 640x480 -> 84x84); reproduce exactly at deployment. Gripper is 0 = open, 1 = closed — verify on hardware first (see the joint-space card for the one-line check). All 200 episodes kept, including the 2 aborted takes and 14 with re-grasps.

Reproducing

cd real_env/yam_teleop && python convert_to_robomimic_hdf5.py \
  --input-dir ~/data/yam-200-ee --output-path ../../data/yam_ee_200.hdf5

cd bspline_policy && python train.py \
  --config-name=yam_pick_ee_200_bspline \
  hydra.run.dir=../outputs/yam_pick_ee_200 \
  training.resume=false logging.mode=offline \
  checkpoint.topk.k=999 dataloader.persistent_workers=True

Give this task config a cache_suffix distinct from every other run, or it will silently load a stale zarr cache.

Citation

@article{han2026b,
  title={B-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations},
  author={Han, Xiaoshen and Xiong, Haoyu and Chen, Haonan and Liu, Chaoqi and
          Torralba, Antonio and Zhu, Yuke and Du, Yilun},
  journal={arXiv preprint arXiv:2607.09648},
  year={2026}
}