让机器人通过可控碰撞，抓取被遮挡的物体。
Collision-Inclusive Manipulation Planning for Occluded Object Grasping via Compliant Robot Motions
- 利用柔顺运动吸收碰撞，通过重复操作缩小不确定区域
- 在单臂与双臂系统上均实现无初始可行抓取的遮挡物抓取
- 适合复杂环境中的物理交互任务，如工业装配或救援
机器人操作研究探索了需主动与环境发生接触的高接触场景，以完成传统避障方法无法解决的任务。通过引入柔顺运动，机器人与环境的碰撞更易被容忍并可加以利用，但带来了更多物理不确定性。为应对如遮挡物体抓取等高接触问题并处理相关不确定性，我们提出一种包含碰撞的规划框架，通过粗略建模的碰撞，由笛卡尔阻抗控制吸收能量，引导机器人过渡到目标任务构型。通过战略性利用环境约束，并在任务重复形成的操控漏斗内探索，该框架有效降低了物理与感知不确定性。在单臂和双臂真实系统上的实验表明，该框架能高效解决多种现实中的遮挡抓取问题，即使初始时不存在可行抓取姿态。
原文摘要 · Abstract (English)
Robotic manipulation research has investigated contact-rich problems and strategies that require robots to intentionally collide with their environment, to accomplish tasks that cannot be handled by traditional collision-free solutions. By enabling compliant robot motions, collisions between the robot and its environment become more tolerable and can thus be exploited, but more physical uncertainties are introduced. To address contact-rich problems such as occluded object grasping while handling the involved uncertainties, we propose a collision-inclusive planning framework that can transition the robot to a desired task configuration via roughly modeled collisions absorbed by Cartesian impedance control. By strategically exploiting the environmental constraints and exploring inside a manipulation funnel formed by task repetitions, our framework can effectively reduce physical and perception uncertainties. With real-world evaluations on both single-arm and dual-arm setups, we show that our framework is able to efficiently address various realistic occluded grasping problems where a feasible grasp does not initially exist.
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