Interface-Aware Trajectory Reconstruction of Limited Demonstrations for Robot Learning

TL;DR

This paper introduces a trajectory reconstruction method that interprets limited demonstrations from low-dimensional interfaces to generate full-control robot motions, improving efficiency and respecting user intent.

Contribution

It presents a novel algorithm that reconstructs full-control trajectories from limited demonstrations by considering task, environment, and interface constraints.

Findings

Reconstructed trajectories are faster and more efficient.

The method respects user preferences in robot motion.

Effective across different control interfaces and robot types.

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.