Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.02915 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908350709497856 |
|---|---|
| author | Han, Beining Joshi, Abhishek Deng, Jia |
| author_facet | Han, Beining Joshi, Abhishek Deng, Jia |
| contents | Tactile sensing is an important sensing modality for robot manipulation. Among different types of tactile sensors, magnet-based sensors, like u-skin, balance well between high durability and tactile density. However, the large sim-to-real gap of tactile sensors prevents robots from acquiring useful tactile-based manipulation skills from simulation data, a recipe that has been successful for achieving complex and sophisticated control policies. Prior work has implemented binarization techniques to bridge the sim-to-real gap for dexterous in-hand manipulation. However, binarization inherently loses much information that is useful in many other tasks, e.g., insertion. In our work, we propose GCS, a novel sim-to-real technique to learn contact-rich skills with dense, distributed, 3-axis tactile readings. We evaluate our approach on blind insertion tasks and show zero-shot sim-to-real transfer of RL policies with raw tactile reading as input. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-shot Sim2Real Transfer for Magnet-Based Tactile Sensor on Insertion Tasks Han, Beining Joshi, Abhishek Deng, Jia Robotics Tactile sensing is an important sensing modality for robot manipulation. Among different types of tactile sensors, magnet-based sensors, like u-skin, balance well between high durability and tactile density. However, the large sim-to-real gap of tactile sensors prevents robots from acquiring useful tactile-based manipulation skills from simulation data, a recipe that has been successful for achieving complex and sophisticated control policies. Prior work has implemented binarization techniques to bridge the sim-to-real gap for dexterous in-hand manipulation. However, binarization inherently loses much information that is useful in many other tasks, e.g., insertion. In our work, we propose GCS, a novel sim-to-real technique to learn contact-rich skills with dense, distributed, 3-axis tactile readings. We evaluate our approach on blind insertion tasks and show zero-shot sim-to-real transfer of RL policies with raw tactile reading as input. |
| title | Zero-shot Sim2Real Transfer for Magnet-Based Tactile Sensor on Insertion Tasks |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.02915 |