{"ID":3083662,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T04:10:26.231441825Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06281","arxiv_id":"2606.06281","title":"Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation","abstract":"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 manipulation learning remains underexplored. We present Multi-Resolution Tactile Sensing (MiTaS), a representation framework that leverages multiple tactile sensors 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 information 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 results 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 attention analysis reveals the importance of different sensors throughout task execution, validating our multi-resolution tactile sensing approach. Project Page: http://mitas-touch.github.io.","short_abstract":"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 manipulation learning remains underexplored. We present Multi-Resolution Tactile Sensing...","url_abs":"https://arxiv.org/abs/2606.06281","url_pdf":"https://arxiv.org/pdf/2606.06281v1","authors":"[\"Rickmer Krohn\",\"Erik Helmut\",\"Niklas Funk\",\"Jan Peters\",\"Vignesh Prasad\",\"Georgia Chalvatzaki\"]","published":"2026-06-04T15:23:03Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Transformer\"]","has_code":false}