Generation of comfortable lifting motion for a human transfer assistant robot

Moving a patient between a bed and a wheel-chair is one of the most burdensome nursing-care tasks for caregivers. Various kinds of transfer assistant robots have been proposed previously. However, designing comfortable transfer motions for individuals has not been intensively studied due to the difficulty of modeling such physical human-robot interactions in conjunction with user preference. To lift a patient from a bed, a robot should raise the patient’s body from the supine posture to a holding posture. In this research, we proposed two different methods for generating lifting motions comfortable for users: (1) a model-based method for estimating the holding posture, and (2) a model-free method based on Bayesian optimization for generating the raising motion. A comfortable holding posture can be generated using human-robot interactive models by reducing the load and pain sensation in simulation, without user trials. To reduce the load, a musculoskeletal model is developed to estimate muscle force considering human-robot physical interactions. To reduce pain, a softness distribution model of the human body is also created to control the robot to contact the human body at a softer place. The motion for raising the human body from the supine posture to the estimated holding posture can be generated with the model-free method from the user’s feedback (scalar value) in terms of the comfort level. Using Bayesian optimization, the number of required trials with physical interactions between user and robot can be reduced to ease the user’s burden. To verify the effectiveness of the proposed methods, we conducted experiments with a dual arm robotic system and human subjects. We search for the most comfortable holding posture based on the developed human models and compare it with the measured value of subjects in the experiment. To generate the raising motion, the experimental result shows that our method can optimize a user-comfortable care motion controller for each individual efficiently.