Learning Bimanual Manipulation Policies for Bathing Bed-bound People

Abstract

Assistive robots hold promise in enhancing the quality of life for older adults and people with mobility impairments in daily bed bathing routines. When providing bathing assistance to bed-bound people, human caregivers often support the joints when lifting the arms and legs to properly wash and dry occluded areas. This research introduces a novel approach to robotic bed bathing manipulation, where a bimanual robot learns to lift a target limb while controlling a cleaning tool to bath the surface within safe force bounds. To ensure safe, cooperative bath manipulation, our work combines Multi-Agent Reinforcement Learning (MARL) framework with a variable impedance action space enabling adaptive interaction with the environment and carefully-designed reward functions regulating contact force on the human body. Simulation results demonstrate improved bathing area coverage compared to unimanual models and exhibit great adaptability to contact-rich interaction within a safe force boundary. We validate our approach across various human body sizes, showcasing its generalizability. We also transfer our models to a physical Baxter robot bathing a medical-grade manikin. We further incorporate a force tracking controller with the trained models to enhance adaptation to noisy real-world bathing scenarios. To the best of our knowledge, this is the first robot-assisted bed bathing application that performs autonomous bathing around the human body using bimanual robot arms.