Fine Manipulation Using a Tactile Skin: Learning in Simulation and Sim-to-Real Transfer
<|reference_start|>Fine Manipulation Using a Tactile Skin: Learning in Simulation and Sim-to-Real Transfer: 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.<|reference_end|>
@article{kasolowsky2024fine,
    title={Fine Manipulation Using a Tactile Skin: Learning in Simulation and
  Sim-to-Real Transfer},
    author={Ulf Kasolowsky and Berthold B"auml},
    journal={arXiv preprint arXiv:2409.12735},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12735},
    primaryClass={cs.RO}
}