Articutool: Proactive Verification and Decoupled Control for Robust Robot-Assisted Feeding

Abstract

For individuals with motor impairments, general-purpose assistive robots can offer increased independence. However, the practical utility of such systems can be undermined if they are unable to reliably handle common foods, leading to spillage that negatively impacts the user’s dining experience. We propose that decoupling gross arm transport from fine-grained tool manipulation can enhance reliability. To this end, this paper introduces the Articutool: a modular, untethered, and locally intelligent 2-DOF wrist that a 6-DOF arm can temporarily equip to form a decoupled 8-DOF system. This decoupled approach separates the concerns of gross arm transport from fine-grained tool manipulation, empowering the arm’s planner to find robust paths while the tool’s onboard controller maintains utensil orientation. Our “plan-then-verify” control methodology proactively checks the arm’s plans against the Articutool’s kinematic and dynamic limitations to reduce the likelihood of spills before they happen. Our large-scale simulation benchmark, which isolates the challenging constrained-transport phase of feeding, demonstrates that this decoupled approach achieves a 96.0% transport planning success rate with a median planning time of 4.0 seconds. While monolithic baselines can achieve comparable success rates given sufficient computation time, they are orders of magnitude slower (median 75.7s for 8-DOF), rendering them impractical for real-time interaction. Physical experiments confirm these findings, showing that the system can successfully acquire challenging foods such as noodles and liquids, and achieves a 70.0% meaningful success rate (delivering a spill-free bite that meets an empirically-defined mass threshold) on the end-to-end feeding task, a task on which the baseline’s meaningful success rate was only 10.0%. This work serves as a critical step toward an ecosystem of intelligent, task-specific tools for more capable, general-purpose assistive robots.