On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting

Abstract: The field of robotic manipulation has advanced significantly in the last 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, despite its potential, it remains an underexplored direction. This work therefore proposes a multimodal, visuotactile imitation learning framework capable of efficiently learning 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 show that adding tactile information into the policies significantly improves performance by over 40%, thereby underlining the importance of tactile sensing for contact-rich manipulation tasks. Project website: https://sites.google.com/view/tactile-il .