FeasibleCap: Real-Time Embodiment Constraint Guidance for In-the-Wild Robot Demonstration Collection
This work addresses the problem of inefficient robot demonstration collection for robotics researchers and practitioners by reducing the cost of acquiring usable trajectories.
This paper introduces FeasibleCap, a gripper-in-hand data collection system that provides real-time executability guidance for robot demonstrations without requiring robot hardware, learned models, or AR/VR headsets. It checks reachability, joint-rate limits, and collisions, providing visual and haptic feedback to the user. FeasibleCap significantly improves replay success and reduces infeasible frames, especially in tossing tasks, without sacrificing cross-embodiment transfer.
Gripper-in-hand data collection decouples demonstration acquisition from robot hardware, but whether a trajectory is executable on the target robot remains unknown until a separate replay-and-validate stage. Failed demonstrations therefore inflate the effective cost per usable trajectory through repeated collection, diagnosis, and validation. Existing collection-time feedback systems mitigate this issue but rely on head-worn AR/VR displays, robot-in-the-loop hardware, or learned dynamics models; real-time executability feedback has not yet been integrated into the gripper-in-hand data collection paradigm. We present \textbf{FeasibleCap}, a gripper-in-hand data collection system that brings real-time executability guidance into robot-free capture. At each frame, FeasibleCap checks reachability, joint-rate limits, and collisions against a target robot model and closes the loop through on-device visual overlays and haptic cues, allowing demonstrators to correct motions during collection without learned models, headsets, or robot hardware. On pick-and-place and tossing tasks, FeasibleCap improves replay success and reduces the fraction of infeasible frames, with the largest gains on tossing. Simulation experiments further indicate that enforcing executability constraints during collection does not sacrifice cross-embodiment transfer across robot platforms. Hardware designs and software are available at https://github.com/aod321/FeasibleCap.