Tactile-based Object Retrieval From Granular Media

• URL: http://arxiv.org/abs/2402.04536v2
• Date: Wed, 21 Feb 2024 17:31:22 GMT
• Title: Tactile-based Object Retrieval From Granular Media
• Authors: Jingxi Xu, Yinsen Jia, Dongxiao Yang, Patrick Meng, Xinyue Zhu, Zihan Guo, Shuran Song, Matei Ciocarlie
• Abstract summary: We introduce GEOTACT, a robotic manipulation method capable of retrieving objects buried in granular media. We show that our problem formulation leads to the natural emergence of learned pushing behaviors that the manipulator uses to reduce uncertainty. We also introduce a training curriculum that enables learning these behaviors in simulation, followed by zero-shot transfer to real hardware.
• Score: 17.340244278653785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce GEOTACT, a robotic manipulation method capable of retrieving objects buried in granular media. This is a challenging task due to the need to interact with granular media, and doing so based exclusively on tactile feedback, since a buried object can be completely hidden from vision. Tactile feedback is in itself challenging in this context, due to ubiquitous contact with the surrounding media, and the inherent noise level induced by the tactile readings. To address these challenges, we use a learning method trained end-to-end with simulated sensor noise. We show that our problem formulation leads to the natural emergence of learned pushing behaviors that the manipulator uses to reduce uncertainty and funnel the object to a stable grasp despite spurious and noisy tactile readings. We also introduce a training curriculum that enables learning these behaviors in simulation, followed by zero-shot transfer to real hardware. To the best of our knowledge, GEOTACT is the first method to reliably retrieve a number of different objects from a granular environment, doing so on real hardware and with integrated tactile sensing. Videos and additional information can be found at https://jxu.ai/geotact.

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