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One of the most crucial steps toward achieving human-like manipulation skills in robots is to incorporate compliance into the robot controller. Compliance not only makes the robot&#x2019;s behaviour safe but also makes it more energy efficient. In this direction, the variable impedance control (VIC) approach provides a framework for a robot to adapt its compliance during execution by employing an adaptive impedance law. Nevertheless, autonomously adapting the compliance profile as demanded by the task remains a challenging problem to be solved in practice. In this work, we introduce a reinforcement learning (RL)-based approach called DEVILC (Data-Efficient Variable Impedance Learning Controller) to learn the variable impedance controller through real-world interaction of the robot. More concretely, we use a model-based RL approach in which, after every interaction, the robot iteratively learns a probabilistic model of its dynamics using the Gaussian process regression model. The model is then used to optimize a neural-network policy that modulates the robot&#x2019;s impedance such that the long-term reward for the task is maximized. Thanks to the model-based RL framework, DEVILC allows a robot to learn the VIC policy with only a few interactions, making it practical for real-world applications. In simulations and experiments, we evaluate DEVILC on a Franka Emika Panda robotic manipulator for different manipulation tasks in the Cartesian space. The results show that DEVILC is a promising direction toward autonomously learning compliant manipulation skills directly in the real world through interactions. A video of the experiments is available in the link: https://youtu.be/_uyr0Vye5no

Data-efficient Reinforcement Learning for Variable Impedance Control