Model-based variable impedance learning control for robotic manipulation

The capability to adapt compliance by varying muscle stiffness is crucial for dexterous manipulation skills in humans. Incorporating compliance in robot motor control is crucial for enabling real-world force interaction tasks with human-like dexterity. In this study, we introduce a novel approach, we call “deep Model Predictive Variable Impedance Controller (MPVIC)” for compliant robotic manipulation, which combines Variable Impedance Control with Model Predictive Control (MPC). The method involves learning a generalized Cartesian impedance model of a robot manipulator through an exploration strategy to maximize information gain. Within the MPC framework, this learned model is utilized to adapt the impedance parameters of a low-level variable impedance controller, thereby achieving the desired compliance behavior for various manipulation tasks without requiring retraining or finetuning. We assess the efficacy of the proposed deep MPVIC approach using a Franka Emika Panda robotic manipulator in simulations and real-world experiments involving diverse manipulation tasks. Comparative evaluations against model-free and model-based reinforcement learning approaches in variable impedance control are conducted, considering aspects such as transferability between tasks and performance. The results demonstrate the effectiveness and potential of the presented approach for advancing robotic manipulation capabilities.