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arXiv · 2602.23287

Interface-Aware Trajectory Reconstruction of Limited Demonstrations for Robot Learning

Demiana R. Barsoum·Mahdieh Nejati Javaremi·Larisa Y. C. Loke·Brenna D. Argall
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Abstract

Assistive robots offer agency to humans with severe motor impairments. Often, these users control high-DoF robots through low-dimensional interfaces, such as using a 1-D sip-and-puff interface to operate a 6-DoF robotic arm. This mismatch results in having access to only a subset of control dimensions at a given time, imposing unintended and artificial constraints on robot motion. As a result, interface-limited demonstrations embed suboptimal motions that reflect interface restrictions rather than user intent. To address this, we present a trajectory reconstruction algorithm that reasons about task, environment, and interface constraints to lift demonstrations into the robot's full control space. We evaluate our approach using real-world demonstrations of ADL-inspired tasks performed via a 2-D joystick and 1-D sip-and-puff control interface, teleoperating two distinct 7-DoF robotic arms. Analyses of the reconstructed demonstrations and derived control policies show that lifted trajectories are faster and more efficient than their interface-constrained counterparts while respecting user preferences.

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Demiana R. Barsoum, Mahdieh Nejati Javaremi, Larisa Y. C. Loke, Brenna D. Argall. 2026-02-26. Interface-Aware Trajectory Reconstruction of Limited Demonstrations for Robot Learning. https://doi.org/10.1145/3757279.3788671

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