模倣学習における触覚センシングの重要性：ロボットによるマッチ点火のケーススタディ
On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting
視覚と触覚を統合したマルチモーダル模倣学習フレームワークを提案し、動的で接触の多いマッチ点火タスクにおいて触覚情報が政策性能を向上させることを示した。
著者: Niklas Funk, Changqi Chen, Tim Schneider, Georgia Chalvatzaki, Roberto Calandra, Jan Peters
分類: cs.RO
原文アブストラクト
The field of robotic manipulation has advanced significantly in recent years. At the sensing level, several novel tactile sensors have been developed, capable of providing accurate contact information. On a methodological level, learning from demonstrations has proven an efficient paradigm to obtain performant robotic manipulation policies. The combination of both holds the promise to extract crucial contact-related information from the demonstration data and actively exploit it during policy rollouts. However, this integration has so far been underexplored, most notably in dynamic, contact-rich manipulation tasks where precision and reactivity are essential. This work therefore proposes a multimodal, visuotactile imitation learning framework that integrates a modular transformer architecture with a flow-based generative model, enabling efficient learning of fast and dexterous manipulation policies. We evaluate our framework on the dynamic, contact-rich task of robotic match lighting - a task in which tactile feedback influences human manipulation performance. The experimental results highlight the effectiveness of our approach and show that adding tactile information improves policy performance, thereby underlining their combined potential for learning dynamic manipulation from few demonstrations. Project website: https://sites.google.com/view/tactile-il .
