Abstract
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner. They require the ability to predict and adapt to one's partner during an interaction. In this work we want to explore these ideas in a human-robot interaction setting in which a robot is required to learn interactive tasks from a combination of observational and kinesthetic learning. To this end, we propose a deep learning framework consisting of a number of components for (1) human and robot motion embedding, (2) motion prediction of the human partner, and (3) generation of robot joint trajectories matching the human motion. As long-term motion prediction methods often suffer from the problem of regression to the mean, our technical contribution here is a novel probabilistic latent variable model which does not predict in joint space but in latent space. To test the proposed method, we collect human-human interaction data and human-robot interaction data of four interactive tasks “hand-shake,” “hand-wave,” “parachute fist-bump,” and “rocket fist-bump.” We demonstrate experimentally the importance of predictive and adaptive components as well as low-level abstractions to successfully learn to imitate human behavior in interactive social tasks.
Highlights
Physical human-robot interaction requires the robot to actively engage in joint action with human partners
We propose a deep generative model approach to imitation learning of interactive tasks
Our experiments indicate that human-robot interaction (HRI) requires adaptive models which take the human motion and task dynamics into account
Summary
Physical human-robot interaction requires the robot to actively engage in joint action with human partners. We are interested in robotic learning of physical human-robot tasks which require coordinated actions. While reinforcement learning usually solves this task with help of active exploration by the learning agent, in imitation learning the agent is provided with observations of states and actions from which to learn. These demonstrations can either be generated in the agent’s own state space, e.g., by tele-operation (Argall et al, 2009), or in the demonstrators embodiment, e.g., a human demonstrating actions for a robot. In this work we combine these approaches to teach a robot arm trajectories required for a number of interactive tasks
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