A Probabilistic Model for Cobot Decision Making to Mitigate Human Fatigue in Repetitive Co-Manipulation Tasks

Work-related musculoskeletal disorders (WMSDs) are very common. Repetitive motion, which is often present in industrial work, is one of the main physical causes of WMSDs. It uses the same set of human joints repeatedly, which leads to localized joint fatigue. In this work, we present a framework to plan a policy of a collaborative robot that reduces the human fatigue in the long term, in highly repetitive co-manipulation tasks, while taking into account the uncertainty in the human postural reaction to the robot motion and the partial observability of the human fatigue state. We model the problem using continuous-state Partially Observable Markov Decision Process (POMDP), and use a physics-based digital human simulator to predict the fatigue cost of the possible robot actions. We then use an online planning algorithm to compute the optimal robot policy. We demonstrate our approach on a simulated experiment in which a robot repeatedly carries an object for the human to work on, and the object Cartesian pose needs to be optimized. We compare the policy generated with our approach with a random, a cyclic and a greedy (short-term optimization) policy, for different user profiles. We show that our approach outperforms the other policies on all tested scenarios.