Datasets:

learner1119
/

VINE2_sim_420_dee

---
license: mit
task_categories: [robotics]
tags: [LeRobot, vine2, delta-actions]
---
# VINE2_sim_420_dee
**Delta-EE action relabeling** of [`EndeavoringYoon/VINE2_sim_420`](https://huggingface.co/datasets/EndeavoringYoon/VINE2_sim_420).
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.

	---
	license: mit
	task_categories: [robotics]
	tags: [LeRobot, vine2, delta-actions]
	---

	# VINE2_sim_420_dee

	Delta-EE action relabeling of [`EndeavoringYoon/VINE2_sim_420`](https://huggingface.co/datasets/EndeavoringYoon/VINE2_sim_420).
	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.