Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
Paper • 2304.13705 • Published • 7
Search is not available for this dataset
observation.state list | observation.commanded_state list | action list | timestamp float32 0 17.3 | frame_index int64 0 346 | episode_index int64 0 3 | index int64 0 1.09k | task_index int64 0 0 |
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End of preview. Expand in Data Studio
FR3 Leader-Command Pilot
Four FR3 + GELLO teleoperation episodes recorded with the corrected action convention:
action is the GELLO leader's commanded joint position, following ACT/ALOHA. This is a
pilot run verifying the fix before full-scale collection — not a training set.
Contents
| Robot / teleop | Franka Research 3 + GELLO leader arm |
| Task | pick up the skyblue cup and place it on the yellow bowl |
| Episodes / frames | 4 / 1092 (319, 208, 347, 218) — all success=True |
| Rate | 20 Hz |
| Cameras | agent (RealSense D455) + wrist (D405), 256×256 RGB |
| Size | ~6 MB |
| Feature | Shape | Meaning |
|---|---|---|
observation.state |
(8,) | Follower measured joints + gripper |
observation.commanded_state |
(8,) | GELLO leader command |
action |
(8,) | = observation.commanded_state (leader command) |
Why the convention matters
The follower runs behind a critically-damped reference filter (ωn = 10 rad/s,
v_max = 1.0 rad/s) and trails the leader by roughly 4 ticks. In these episodes the
leader–follower gap is median 0.011 rad, max 0.48 rad — that lead is what actually
drags the arm forward, and it exists only in the command.
An earlier version of this collector stored the follower's realized state[t+1] as the
action instead. A policy trained that way can only ever emit "one tick past where the arm
already is", so it cannot command a catch-up: its tracking lag re-anchors at every replan
and the motion regresses backwards at the replan period.
Closed-loop simulation with a perfect policy, on this hardware's actual filter, comparing the two conventions on real episodes:
| Convention | boundary regression | chunk efficiency | wall-clock |
|---|---|---|---|
realized state[t+1] (old) |
+2.50 steps, 100 % of boundaries | 83 % | 131 % |
| leader command (this dataset) | −1.00 steps (stays ahead) | ~100 % | 99.9 % |
The −1.00 is the perfect-tracking signature: each chunk ends exactly at its commanded frontier and the next begins one step beyond it. The 31 % slowdown is gone.
The full analysis, and a dataset that freezes the old convention for comparison, is at
knu-physical-ai/fr3-action-space-case-study.
Limitations
- 4 episodes. A pilot. Do not train or benchmark on it.
- Single task, single scene layout.
- The leader command is rougher than the realized trajectory (~8× the jerk RMS). The reference filter removes this downstream, but whether a policy fits it cleanly is still to be verified.
References
- Zhao, Kumar, Levine, Finn. Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (ACT / ALOHA) — "It is important to use the leader joint positions instead of the follower's, because the amount of force applied is implicitly defined by the difference between them."
Collected with GELLO on an FR3 via libfranka; converted with LeRobot v3.0.
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