SensorPerch：随时随地感知重要之处

中文摘要 AI 辅助

现有的机器人感知受限于安装在机器人上或固定在环境中的传感器，将感知局限于有限的视角。随着机器人执行任务日益多样，最佳信息视角不断变化，现有方式难以满足。为此提出SensorPerch，将传感器视为独立物理实体，使其与机器人及环境解耦，实现主动感知。它包括可附着不同表面的轻量级、无线、可重构传感器平台及视角选择框架。在对象耦合感知和策略耦合感知两类任务中验证了该范式，能在机器人当前位置之外持续检测对象状态，还能为不同策略构建特定视角，成功率与使用预言视角相当。

英文摘要

Existing robotic perception is constrained by sensors that are either robot-mounted or permanently fixed in the environment, locking perception to a limited set of viewpoints. Yet as robots perform increasingly diverse tasks, the most informative viewpoint shifts from one task to the next-often somewhere onboard sensor and static infrastructure can not readily satisfy. To address this gap, we propose SensorPerch, a novel realization of active perception that decouples sensing from both the robot embodiment and the environment by treating sensors as independent physical entities that the robot can autonomously detach and re-attach within the environment. SensorPerch presents one realization of this paradigm: a lightweight, wireless, reconfigurable sensor platform that can perch on diverse surfaces, paired with a viewpoint-selection framework that determines task-optimal sensor placements. Together, these enable robots to construct task-relevant viewpoints on demand, independent of the robot's current position and available fixed infrastructure. We demonstrate the paradigm on two task classes: (i) object-coupled perception, where SensorPerch enables persistent object-state detection beyond the robot's current position, achieving successful event detection even when the robot is not nearby; and (ii) policy-coupled perception, where SensorPerch allows robots to construct diverse, policy-specific viewpoints for various policies, achieving success rates comparable to those obtained using oracle viewpoints.

发表机构

• Cornell University（康奈尔大学）

机构由 AI 辅助整理，请以论文原文为准。