Fine Manipulation Using a Tactile Skin: Learning in Simulation and Sim-to-Real Transfer
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917779889717248 |
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| author | Kasolowsky, Ulf Bäuml, Berthold |
| author_facet | Kasolowsky, Ulf Bäuml, Berthold |
| contents | We want to enable fine manipulation with a multi-fingered robotic hand by using modern deep reinforcement learning methods. Key for fine manipulation is a spatially resolved tactile sensor. Here, we present a novel model of a tactile skin that can be used together with rigid-body (hence fast) physics simulators. The model considers the softness of the real fingertips such that a contact can spread across multiple taxels of the sensor depending on the contact geometry. We calibrate the model parameters to allow for an accurate simulation of the real-world sensor. For this, we present a self-contained calibration method without external tools or sensors. To demonstrate the validity of our approach, we learn two challenging fine manipulation tasks: Rolling a marble and a bolt between two fingers. We show in simulation experiments that tactile feedback is crucial for precise manipulation and reaching sub-taxel resolution of < 1 mm (despite a taxel spacing of 4 mm). Moreover, we demonstrate that all policies successfully transfer from the simulation to the real robotic hand. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12735 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fine Manipulation Using a Tactile Skin: Learning in Simulation and Sim-to-Real Transfer Kasolowsky, Ulf Bäuml, Berthold Robotics We want to enable fine manipulation with a multi-fingered robotic hand by using modern deep reinforcement learning methods. Key for fine manipulation is a spatially resolved tactile sensor. Here, we present a novel model of a tactile skin that can be used together with rigid-body (hence fast) physics simulators. The model considers the softness of the real fingertips such that a contact can spread across multiple taxels of the sensor depending on the contact geometry. We calibrate the model parameters to allow for an accurate simulation of the real-world sensor. For this, we present a self-contained calibration method without external tools or sensors. To demonstrate the validity of our approach, we learn two challenging fine manipulation tasks: Rolling a marble and a bolt between two fingers. We show in simulation experiments that tactile feedback is crucial for precise manipulation and reaching sub-taxel resolution of < 1 mm (despite a taxel spacing of 4 mm). Moreover, we demonstrate that all policies successfully transfer from the simulation to the real robotic hand. |
| title | Fine Manipulation Using a Tactile Skin: Learning in Simulation and Sim-to-Real Transfer |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.12735 |