Publikation
On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting
Niklas Funk; Changqi Chen; Tim Schneider; Georgia Chalvatzaki; Roberto Calandra; Jan Peters
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2504.13618, Pages 1-9, arXiv, 2025.
Zusammenfassung
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 accu-
rate 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 imita-
tion 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 dy-
namic manipulation from few demonstrations. Project website:
https://sites.google.com/view/tactile-il.