A B-spline Policy UNet diffusion policy trained on Dimios45/yam-pick-duster-ee for a single-arm I2RT YAM: pick up the duster.

This model emits absolute Cartesian poses and requires inverse kinematics on the robot. The joint-space counterpart — same 50 takes, no IK needed — is Dimios45/yam-pick-duster-bspline-joint. The pair exists to compare action spaces on identical demonstrations.

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, so _convert_actions expands the 7-D raw action to 10-D. infer_action_meta resolves this to single_yam_rot6d (verified on the published checkpoint).

Network output is (16, 11): column 0 is the knot vector in units of 10 Hz frames, columns 1–10 are control points.

Loss: 0.829 (ep0) → 0.014 (100) → 0.009 (200) → 0.006 (300) → 0.005 (400) → 0.003 (500–600), final in-epoch 0.00153.

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.

Actions are decoded to arm_pos + arm_quat (from rot6d) + gripper and resolved by the pyroki velocity-IK step in yam_server at 100 Hz. This is the upstream single_yam_rot6d path, so no joint-space patches are needed for the action decoding itself — but the top_image camera still has to be wired into real_env.get_obs, since upstream RealEnv only populates wrist_image and a missing RGB key is silently replaced with a black frame rather than raising.

A 16-knot chunk spans ~1.1 s at 10 Hz, so this model replans ~2.5× less often than the joint-space one.

The dataset card states these poses were computed from MuJoCo and disagree with bspline-policy's pyroki/URDF by up to ~9 mm. At deploy the IK will chase a target frame offset from the one the policy was trained against, so expect a systematic positional bias. The TCP frame is the flange origin with a fixed 90° z-rotation, ~13.5 cm from the actual grasp point. Resolve the frame mismatch before trusting fine manipulation, or use the joint-space model, which sidesteps kinematics entirely.

Do not mix this data with yam_server-recorded episodes without resolving that discrepancy.

Trained uncropped (RAW 640×480 → 84×84); reproduce that exactly at deployment. Gripper is 0 = open, 1 = closed — verify on hardware first (see the joint-space card for the one-line check).

Give this task config a cache_suffix distinct from the joint-space run's, or it will silently load the other run's zarr cache.