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.

翻译：接触密集型操作对机器人执行具有挑战性，需要准确的环境感知能力。在某些场景中，视觉信息会受到遮挡。此时机器人无法再通过视觉反馈获取实时场景状态信息，这种情况被称为“盲操作”。本文提出一种新颖的物理驱动的接触感知方法，称为“Contact SLAM”。该方法仅利用触觉传感和场景先验知识，即可估计环境状态并完成操作任务。为最大化探索效率，本文还设计了一种主动探索策略。该策略能逐步降低操作场景中的不确定性。实验结果表明，所提方法在包括高难度精细的插座装配任务和方块推动任务在内的多个接触密集型任务中，均展现出卓越的有效性与精确性。