接触SLAM：基于物理推理的主动触觉探索策略用于机器人精细盲操控任务
Contact SLAM: An Active Tactile Exploration Policy Based on Physical Reasoning Utilized in Robotic Fine Blind Manipulation Tasks
AI总结：
本文提出接触SLAM方法，通过触觉传感和先验知识实现接触密集任务中的盲操控，提升探索效率与准确性。
摘要：
接触密集的操控对机器人来说很难执行，需要准确的环境感知。在某些场景中，视觉会被遮挡。机器人则无法通过视觉反馈获取实时场景状态信息。这被称为『盲操控』。本文提出了一种新的基于物理的接触认知方法，称为『接触SLAM』。它仅利用触觉传感和对场景的先验知识来估计环境状态并实现操控。为了最大化探索效率，本文还设计了主动探索策略。该策略逐步减少操控场景中的不确定性。实验结果证明了所提出方法在几种接触密集任务中的有效性和准确性，包括困难和精细的套筒装配任务和块推动任务。
英文摘要：
Contact-rich manipulation is difficult for robots to execute and requires accurate perception of the environment. In some scenarios, vision is occluded. The robot can then no longer obtain real-time scene state information through visual feedback. This is called ``blind manipulation". In this manuscript, a novel physically-driven contact cognition method, called ``Contact SLAM", is proposed. It estimates the state of the environment and achieves manipulation using only tactile sensing and prior knowledge of the scene. To maximize exploration efficiency, this manuscript also designs an active exploration policy. The policy gradually reduces uncertainties in the manipulation scene. The experimental results demonstrated the effectiveness and accuracy of the proposed method in several contact-rich tasks, including the difficult and delicate socket assembly task and block-pushing task.
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。
