VINE2_real_200_dee

Delta-EE action relabeling of EndeavoringYoon/VINE2_real_200. Everything except the action column (videos, observation.state, intervention, episode metadata, meta/episode_labels.csv, meta/train_eval_split.json) is copied verbatim.

What changed

The original action is an 8-dim absolute joint command (7 arm joints + gripper). Here it is replaced by a 7-dim task-space delta, following the action convention of "Why Does Action Chunking Improve Behavioral Cloning Performance?" (Lazzati et al., 2026, arXiv:2608.02547, Appendix A.4/A.5):

action[t] = [ dx, dy, dz, drx, dry, drz, gripper ]

(dx,dy,dz)    = p_cmd - p_cur                      # translation, world frame [m]
(drx,dry,drz) = axis_angle( R_cmd @ R_cur^T )      # rotation delta, world frame [rad]
gripper       = original action[7], unchanged

where (p_cmd, R_cmd) = FK(commanded joints a[:7]) and (p_cur, R_cur) = FK(measured state s[:7]), both evaluated at the gripper pad centre of the real2sim-calibrated MuJoCo scene (VINE2_data_collection/asset/scene.xml, lift/head joints and base pose fixed to the calibrated task values).

Key property: the delta is referenced to the state at execution time — not to the chunk-start state (the GR00T-style convention the paper explicitly avoids). At deployment, the absolute command is recovered as IK( FK(current state) (+) predicted delta ).

meta/stats.json and the per-episode stats in meta/episodes/ were recomputed for the new action; info.json declares the new shape (7,) and names.

Why

With absolute joint actions, corr(action, state) is about 0.99 — the answer is printed in the observation, which structurally erases non-Markov demonstrator-intent signal. After this relabeling corr drops to about 0.42 and an ACT (chunk 50) trained on the sim variant shows a genuine delayed-prediction advantage (best delay 2 steps at 10 Hz, -9.7 percent val error), which the absolute version does not.

Generated by make_delta_ee_dataset.py (VINE2 workspace), 2026-08-27.