A Soft-Rigid Hybrid Robot-Assisted Feeding System With a Tendon-Driven Continuum Robot

Abstract

Active delivery of food to a human mouth in a controlled and safe manner remains a key challenge for robot-assisted feeding systems (RAFSs). Existing RAFS designs struggle to simultaneously achieve efficiency and safety: rigid manipulators offer fast and accurate motion but risk hazardous contact, while soft robots provide passive compliance at the cost of limited speed or workspace. To meet the specific demands of feeding tasks, we design a tendon-driven continuum robot that allows precise orientation control of the utensil while exhibiting strong passive compliance in position. Integrating it with a 6-DoF rigid robot for fast and long-range positioning, we propose a hybrid RAFS architecture that achieves safe, efficient, and accurate food delivery. Controlling a passive-compliant RAFS to acquire various food is non-trivial: physical modeling struggles with complex interactions between soft robot and food, while typical imitation learning methods lead to discontinuous or distorted movements out of the passive deformation. To handle this, we design a pose-torque learning policy that enables the soft and rigid robots to generate coherent and synchronized movements, offering a case-specific solution to the long-standing challenge of soft robot imitation learning. Experiments show that our method achieve a food acquisition success rate of 76.7%, while user tests with 14 volunteers confirm user preference, marking our RAFS as a practical step toward safe and efficient robotic feeding.