Q-Learning-based model predictive variable impedance control for physical human-robot collaboration

Abstract

Physical human-robot collaboration is increasingly required in many contexts (such as industrial and rehabilitation applications). The robot needs to interact with the human to perform the target task while relieving the user from the workload. To do that, the robot should be able to recognize the human's intentions and guarantee safe and adaptive behavior along the intended motion directions. The robot-control strategies with such attributes are particularly demanded in the industrial field, where the operator guides the robot manually to manipulate heavy parts (e.g., while teaching a specific task). With this aim, this work proposes a Q-Learning-based Model Predictive Variable Impedance Control (Q-LMPVIC) to assist the operators in a physical human-robot collaboration (pHRC) tasks. A Cartesian impedance control loop is designed to implement a decoupled compliant robot dynamics. The impedance control parameters (i.e., setpoint and damping parameters) are then optimized online in order to maximize the performance of the pHRC. For this purpose, an ensemble of neural networks is designed to learn the modeling of the human-robot interaction dynamics while capturing the associated uncertainties. The derived modeling is then exploited by the model predictive controller (MPC), enhanced with the stability guarantees by means of Lyapunov constraints. The MPC is solved by making use of a Q-Learning method that, in its online implementation, uses an actor-critic algorithm to approximate the exact solution. Indeed, the Q-learning method provides an accurate and highly efficient solution (in terms of computational time and resources). The proposed approach has been validated through experimental tests, in which a Franka EMIKA panda robot has been used as a test platform. Each user was asked to interact with the robot along the controlled vertical z Cartesian direction. The proposed controller has been compared with a model-based reinforcement learning variable impedance controller (MBRLC) previously developed by some of the authors in order to evaluate the performance. As highlighted in the achieved results, the proposed controller is able to improve the pHRC performance. Additionally, two industrial tasks (a collaborative assembly and a collaborative deposition task) have been demonstrated to prove the applicability of the proposed solution in real industrial scenarios.