--- license: apache-2.0 tags: [robotics, lerobot, real-robot] --- # 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 `__`. 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.