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arXiv 2023年

作者： Prasad, Vignesh Heitlinger, Lea Koert, Dorothea Stock-Homburg, Ruth Peters, Jan Chalvatzaki, Georgia Department of Computer Science TU Darmstadt Germany Chair for Marketing and Human Resource Management Department of Law and Economics TU Darmstadt Germany Department of Computer Science TU Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany Darmstadt Germany Interactive Robot Perception and Learning group Department of Computer Science TU Darmstadt Germany Center for Mind Brain & Behavior Uni. Marburg JLU Giessen Germany

This article presents a method for learning wellcoordinated Human-robot Interaction (HRI) from Human- Human Interactions (HHI). We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational Autoencoder to model a joint distribution over the interacting agents. We leverage the interaction dynamics learned from HHI to learn HRI and incorporate the conditional generation of robot motions from human observations into the training, thereby predicting more accurate robot trajectories. The generated robot motions are further adapted with Inverse Kinematics to ensure the desired physical proximity with a human, combining the ease of joint space learning and accurate task space reachability. For contact-rich interactions, we modulate the robot's stiffness using HMM segmentation for a compliant interaction. We verify the effectiveness of our approach deployed on a Humanoid robot via a user study. Our method generalizes well to various humans despite being trained on data from just two humans. We find that Users perceive our method as more human-like, timely, and accurate and rank our method with a higher degree of preference over other baselines. © 2023, CC BY-NC-SA.

关键词： Anthropomorphic robots

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