Imitation Learning with Additional Constraints on Motion Style using Parametric Bias

Abstract

Imitation learning is one of the methods for reproducing human demonstration adaptively in robots. So far, it has been found that generalization ability of the imitation learning enables the robots to perform tasks adaptably in untrained environments. However, motion styles such as motion trajectory and the amount of force applied depend largely on the dataset of human demonstration, and settle down to an average motion style. In this study, we propose a method that adds parametric bias to the conventional imitation learning network and can add constraints to the motion style. By experiments using PR2 and the musculoskeletal humanoid MusashiLarm, we show that it is possible to perform tasks by changing its motion style as intended with constraints on joint velocity, muscle length velocity, and muscle tension.

Accepted at IEEE Robotics and Automation Letters

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• Deep Predictive Model Learning with Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
• Development of a Whole-body Work Imitation Learning System by a Biped and Bi-armed Humanoid
• Characteristics, Management, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi