Publikation
Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation
Rickmer Krohn; Erik Helmut; Niklas Funk; Jan Peters; Vignesh Prasad; Georgia Chalvatzaki
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2606.06281, Pages 1-20, arXiv, 2026.
Zusammenfassung
Touch sensing is beneficial for solving a wide variety of manipulation
tasks. While there exists a wide range of tactile sensors with different properties,
exploiting the fusion of multiple heterogeneous tactile sensors to improve ma-
nipulation learning remains underexplored. We present Multi-Resolution Tactile
Sensing (MiTaS), a representation framework that leverages multiple tactile sen-
sors operating at different temporal resolutions in order to solve complex contact-
rich manipulation tasks. We propose a novel architecture using modality-specific
convolutional stems and transformer-based fusion that effectively fuses informa-
tion from an RGB camera stream, a vision-based GelSight Mini sensor and a
high-frequency event-based Evetac sensor. This multi-sensor representation then
conditions a flow-matching policy for solving downstream tasks. Experimental re-
sults across five contact-rich manipulation tasks demonstrate the effectiveness of
multi-resolution tactile features in imitation learning. MiTaS achieves an average
success rate of 80 %, while vision-only (31 %) and visual-tactile (54 %) baselines
cannot solve the task reliably. Co-training a visuo-tactile model with multi-tactile
data boosts performance by over 10 % in certain tasks, without having access to
the Evetac sensor during policy evaluation. A detailed sensor-reading and atten-
tion analysis reveals the importance of different sensors throughout task execution,
validating our multi-resolution tactile sensing approach.