Robots often face manipulation tasks in environments where vision is inadequate due to clutter, occlusions, or poor lighting--for example, reaching a shutoff valve at the back of a sink cabinet or locating a light switch above a crowded shelf. In such settings, robots, much like humans, must rely on contact feedback to distinguish free from occupied space and navigate around obstacles. Many of these environments often exhibit strong structural priors--for instance, pipes often span across sink cabinets--that can be exploited to anticipate unseen structure and avoid unnecessary collisions. We present a theoretically complete and empirically efficient framework for manipulation in the blind that integrates contact feedback with structural priors to enable robust operation in unknown environments. The framework comprises three tightly coupled components: (i) a contact detection and localization module that utilizes joint torque sensing with a contact particle filter to detect and localize contacts, (ii) an occupancy estimation module that uses the history of contact observations to build a partial occupancy map of the workspace and extrapolate it into unexplored regions with learned predictors, and (iii) a planning module that accounts for the fact that contact localization estimates and occupancy predictions can be noisy, computing paths that avoid collisions and complete tasks efficiently without eliminating feasible solutions. We evaluate the system in simulation and in the real world on a UR10e manipulator across two domestic tasks--(i) manipulating a valve under a kitchen sink surrounded by pipes and (ii) retrieving a target object from a cluttered shelf. Results show that the framework reliably solves these tasks, achieving up to a 2x reduction in task completion time compared to baselines, with ablations confirming the contribution of each module.

翻译：机器人常在视觉受限的环境中执行操作任务，例如因杂物遮挡、视线遮蔽或光照不足导致视觉信息不足的场景——比如伸手够到水槽柜后方的关闭阀门，或在拥挤的货架上方定位电灯开关。在此类情境下，机器人（与人类相似）必须依赖接触反馈来区分空闲空间与障碍区域，并规避障碍物。这类环境通常具有显著的结构先验特征（例如水槽柜内常布设管道），可利用这些特征预测不可见结构并避免不必要的碰撞。本文提出一种理论完备且实证高效的盲操作框架，通过将接触反馈与结构先验相结合，实现在未知环境中的鲁棒操作。该框架包含三个紧密耦合的组件：（i）接触检测与定位模块：利用关节力矩传感结合接触粒子滤波器实现接触检测与定位；（ii）占据状态估计模块：利用接触观测历史构建工作空间的部分占据地图，并通过学习型预测器将其外推至未探索区域；（iii）规划模块：考虑接触定位估计与占据预测可能存在噪声的特性，在避免碰撞的前提下计算高效完成任务的路径，同时不排除可行解。我们在仿真环境与真实世界的UR10e机械臂上对该系统进行了评估，涵盖两项家庭任务：（i）在布满管道的厨房水槽下方操作阀门；（ii）从杂乱货架上取回目标物体。实验结果表明，该框架能可靠完成这些任务，与基线方法相比任务完成时间最高减少2倍，消融实验验证了各模块的贡献。