[3Q5-GS-8] Robot and real worlds:

[3Q5-GS-8-01] A Preliminary Study on Weighted Average Hierarchical Model in Bilateral Control-based Imitation Learning

〇Yu-Han Shu¹, Koki Inami¹, Koki Yamane¹, Sho Sakaino¹, Toshiaki Tsuji² (1. University of Tsukuba, 2. Saitama University)

Keywords:Imitation Learning, Bilateral Control, Motion Planning, intelligent robotics

Imitation learning has gained attention as a method for enabling robots to efficiently acquire human operational skills and replicate human motion techniques. However, training imitation learning models typically requires a large amount of teaching data, and re-teaching is necessary for each new task, which presents significant challenges. This study proposes an extended approach to bilateral control-based imitation learning by introducing a hierarchical model to achieve complex motions through the weighted averaging of action primitives. The proposed model is structured into two layers: the lower layer consists of multiple models that learn individual action primitives, while the upper layer is responsible for long-term motion planning and determining the optimal proportions for combining these action primitives. This method enables transfer learning across different tasks. In the validation experiment for the pick-and-place task, the upper layer was re-trained to perform a motion moving from right to left using the action primitives previously learned for the motion from left to right. As a result, autonomous motion was successfully achieved with a small amount of teaching data, thereby demonstrating the effectiveness of the proposed method.