基于触觉的颗粒介质中物体检索
Tactile-based Object Retrieval From Granular Media
AI总结：
GEOTACT通过触觉反馈学习实现从颗粒介质中检索未知形状物体，展示了零样本迁移和复杂物体抓取能力。
摘要：
我们介绍了GEOTACT，这是首个能够从颗粒环境中抓取和检索潜在未知形状物体的机器人系统。尽管在许多应用中，从采矿和探索到搜索和救援，这种与颗粒介质的交互具有重要价值，但这种交互困难源于视觉遮挡和噪声接触信号带来的不确定性。为了解决这些挑战，我们使用仅依赖触觉反馈的学习方法，通过模拟传感器噪声进行端到端训练。我们证明了我们的问题表述自然地导致了学习到的推动行为，使执行器能够通过减少不确定性并将物体引导至稳定抓取，即使在虚假和噪声触觉读数下。我们引入了一个训练课程，使学习能够在模拟颗粒环境中启动，从而实现对真实硬件的零样本迁移。尽管仅在七个具有简单形状的物体上进行训练，我们的方法已被证明能够成功检索35种不同的物体，包括刚性、变形和复杂形状的可变形和关节物体。视频和额外信息可在https://jxu.ai/geotact找到。
英文摘要：
We introduce GEOTACT, the first robotic system capable of grasping and retrieving objects of potentially unknown shapes buried in a granular environment. While important in many applications, ranging from mining and exploration to search and rescue, this type of interaction with granular media is difficult due to the uncertainty stemming from visual occlusion and noisy contact signals. To address these challenges, we use a learning method relying exclusively on touch feedback, 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 introduce a training curriculum that bootstraps learning in simulated granular environments, enabling zero-shot transfer to real hardware. Despite being trained only on seven objects with primitive shapes, our method is shown to successfully retrieve 35 different objects, including rigid, deformable, and articulated objects with complex shapes. Videos and additional information can be found at https://jxu.ai/geotact.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
