TACTIC: Understanding Tactile Encoders and Conditioning for Contact-rich Robot Manipulation Policies

Abstract:Tactile information is essential for contact-rich manipulation tasks in robotics. Vision-based tactile sensors make it particularly easy to design end-to-end manipulation policies with tactile sensing, as they enable the use of existing encoders from computer vision. However, this has led to a huge variety of architectures, training datasets, and evaluation protocols, making it difficult to determine which design choices best encode touch. In this work, we address this gap and present a comprehensive study of tactile encoders and fusion strategies across various contact-rich manipulation tasks in real-world experiments. To enable a controlled comparison, we train and evaluate all models under the same pipeline and experimental setup, comprising more than 2000 real-world rollouts. Our results go beyond other studies that only compare simulation performance, which does not necessarily translate to real-world settings, where large-scale evaluations are needed to obtain reliable statistics. Our key finding is that there is no universally optimal representation or fusion strategy for encoding visual-tactile. Instead, the best encoder backbone and fusion scheme depend strongly on the task.

Robotics (cs.RO)

arXiv:2609.30969 [cs.RO]

(or arXiv:2609.30969v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.30969 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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