ghkim-rlwrld/shell_game_eef_subtask

shell_game — delta EEF, per-frame subtask

Cups hide a red block, the cups are shuffled by hand, and the arm lifts the cup the block ended under. 45 episodes, 18,442 frames.

Delta EEF, 10-dim. Six sentences, assigned per frame:

observe                              6,051 frames
uncover the left cup                 2,203
uncover the middle cup               1,517
uncover the right cup                1,848
move the red cube into the basket    6,823

Labels are derived from state, never from the video: the gripper separates cup (closure 0.120-0.200) from cube (0.440-0.460) with no overlap and exactly two grasps in 45 of 45 episodes, and the cup is read off cartesian y, which is 3-modal. They agree with the human collection sheet on 44 of 45; the one disagreement is an episode where the operator lifted the wrong cup.

Which cup to uncover cannot be derived from the current frame — the shuffle has already happened and every cup looks alike. That is the point of the task, and it is why a planner trained on this needs memory rather than perception.

Converted from the recorder's DROID-schema teleoperation.h5, one episode per directory, with the videos re-encoded all-intra. The raw h5 is the source of record and lives at ghkim-rlwrld/shell_game; anything that needs a different action representation or a fresh label pass has to go back to it, not to this tree.

Cameras are three stereo pairs and only the left eye is shipped here, which is what a policy reading three streams actually loads:

11845904  wrist        looks down over the gripper
37149196  cam_ext_left   external, to the robot's left
34022131  cam_ext_right  external, to the robot's right

Control rate is 9.2 Hz measured. The mp4 containers say 10 fps and are wrong.

Naming

<task>_<action space>_<language>. An earlier set of names took the action space from one dataset and the language from another, so they could not be compared side by side.

	action	language
shell_game_joint_global	7 joints + gripper (8)	one sentence per episode
shell_game_eef_global	delta EEF, xyz + rot6d + gripper (10)	one sentence per episode
shell_game_eef_subtask	delta EEF (10)	per-frame subtask
solve_equation_eef_subtask	delta EEF (10)	per-frame subtask

Why rot6d and not rpy

This recorder's roll sits at +-pi and flips sign 269 times across the 45 shell_game episodes. Regressing the raw angle puts a 2*pi step in the target at every flip. Unwrapping and moving to the first two columns of the rotation matrix leaves 0 discontinuities and takes the roll channel from std 3.08 to 0.039. The delta mask is (9, -1): position and rotation relative to the current state, gripper absolute.

A runtime that wants 7-dim xyz+rpy can recover it exactly — Gram-Schmidt the two 3-vectors, take the third by cross product, then as_euler("xyz"), verified to 4.44e-16 over 41 episodes. Doing that at inference is safe; training on rpy is not, for the reason above.