Adaptation of robot physical behaviour to human fatigue in human-robot co-manipulation

In this paper, we propose a method that allows the robot to adapt its physical behaviour to the human fatigue in human-robot co-manipulation tasks. The robot initially imitates the human to perform the collaborative task in a leader-follower setting, using a feedback about the human motor behaviour. Simultaneously, the robot obtains the skill in online manner. When a predetermined level of human fatigue is detected, the robot uses the learnt skill to take over the physically demanding aspect of the task and contributes to a significant reduction of the human effort. The human, on the other hand, controls and supervises the high-level interaction behaviour and performs the aspects that require the contribution of both agents in such a dynamic co-manipulation setup. The robot adaptation system is based on the Dynamical Movement Primitives, Locally Weighted Regression and Adaptive Frequency Oscillators. The estimation of the human motor fatigue is carried out using a proposed online model, which is based on the human muscle activity measured by the electromyography. We demonstrate the proposed method with experiments on a real-world co-manipulation task with environmental constraints and dynamic uncertainties.