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arXiv 2019年

作者： Berenz, Vincent Bjelic, Ahmed Mainprice, Jim Autonomous Motion Department Max Planck Institute for Intelligent Systems Tübingen Germany Humans to Robots Motion Research Group HRM Stuttgart University Stuttgart Germany

Social robots or collaborative robots that have to interact with people in a reactive way are difficult to program. This difficulty stems from the different skills required by the programmer: To provide an engaging user experience the behavior must include a sense of aesthetics while robustly operating in a continuously changing environment. The Playful framework [1] allows to compose such dynamic behaviors using a basic set of action and perception primitives. Within this framework, a behavior is encoded as a list of declarative statements corresponding to high level sensory-motor couplings. To facilitate non expert users to program such behaviors, we propose a Learning from Demonstration (LfD) technique that maps motion capture of humans directly to a Playful script. The approach proceeds by identifying the sensory-motor couplings that are active at each step using the Viterbi path in a Hidden Markov Model (HMM). Given these activation patterns, binary classifiers called evaluations are trained to associate activations to sensory data. Modularity is increased by clustering the sensory-motor couplings, leading to a hierarchical tree structure. The novelty of the proposed approach is that the learned behavior is encoded not in terms of trajectories in a task space, but as couplings between sensory information and high level motor actions. This provides advantages in terms of behavioral generalization and reactivity displayed by the robot. Copyright © 2019, The Authors. All rights reserved.

关键词： Couplings

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Learning Sensory-Motor Associations from Demonstration

Learning Sensory-Motor Associations from Demonstration

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Vincent Berenz Ahmed Bjelic Lahiru Herath Jim Mainprice Autonomous Motion Department Max Planck Institute for Intelligent Systems Tübingen Germany Humans to Robots Motion Research Group HRM MLR Stuttgart University Stuttgart Germany Department of Control Systems Engineering University of Siegen Siegen Germany

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

We propose a method which generates reactive robot behavior learned from human demonstration. In order to do so, we use the Playful programming language which is based on the reactive programming paradigm. This allows us to represent the learned behavior as a set of associations between sensor and motor primitives in a human readable script. Distinguishing between sensor and motor primitives introduces a supplementary level of granularity and more importantly enforces feedback, increasing adaptability and robustness. As the experimental section shows, useful behaviors may be learned from a single demonstration covering a very limited portion of the task space.

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