动作感知的估计与规划：通过四足机器人腿部操作清理楼梯上的杂物
Action-Informed Estimation and Planning: Clearing Clutter on Staircases via Quadrupedal Pedipulation
AI总结：
针对四足机器人腿部操作中物体被遮挡的问题，提出交互感知的状态估计与感知-动作闭环框架，利用本体感觉预测物体位移，实现楼梯杂物的有效清理，提升任务成功率与跟踪精度。
摘要：
为了让机器人在密集杂乱的环境中自主运行，它们必须对障碍物进行推理并可能与其进行物理交互以清理路径。在具有挑战性的地形（如杂乱的楼梯）上安全地清理路径需要受控的交互。例如，四足机器人用一条腿将物体推开，同时用其余三条腿保持稳定姿态。然而，紧密耦合的物理动作（如单腿推挤）会对系统产生新的约束，这些约束在设计时可能难以预测。在这项工作中，我们提出了一种新方法来解决这样一个约束：当四足机器人用其一条腿推动物体时，被推物体在操作过程中会从机器人的传感器视野中遮挡。为了应对这一挑战，我们提出了一个紧密耦合的感知-动作框架，使机器人能够感知杂物、推理可行的推挤路径并执行清理动作。我们的核心贡献是一个交互感知的状态估计循环，该循环利用关于脚部接触和腿部位置的本体感觉反馈来预测遮挡期间物体的位移。该预测引导感知系统在交互后稳健地重新检测物体，从而在动作和感知之间形成闭环，即使在部分推挤后也能实现准确跟踪。利用这种反馈，机器人可以从物理结果中学习，如果推挤因物体过重而失败，则将物体重新分类为不可移动。我们展示了在波士顿动力Spot机器人上实施我们方法的结果，表明与开环基线相比，我们的交互感知方法在楼梯上推挤物体时实现了更高的任务成功率和跟踪精度。
英文摘要：
For robots to operate autonomously in densely cluttered environments, they must reason about and potentially physically interact with obstacles to clear a path. Safely clearing a path on challenging terrain, such as a cluttered staircase, requires controlled interaction. For example, a quadrupedal robot that pushes objects out of the way with one leg while maintaining a stable stance with its three other legs. However, tightly coupled physical actions, such as one-legged pushing, create new constraints on the system that can be difficult to predict at design time. In this work, we present a new method that addresses one such constraint, wherein the object being pushed by a quadrupedal robot with one of its legs becomes occluded from the robot's sensors during manipulation. To address this challenge, we present a tightly coupled perception-action framework that enables the robot to perceive clutter, reason about feasible push paths, and execute the clearing maneuver. Our core contribution is an interaction-aware state estimation loop that uses proprioceptive feedback regarding foot contact and leg position to predict an object's displacement during the occlusion. This prediction guides the perception system to robustly re-detect the object after the interaction, closing the loop between action and sensing to enable accurate tracking even after partial pushes. Using this feedback allows the robot to learn from physical outcomes, reclassifying an object as immovable if a push fails due to it being too heavy. We present results of implementing our approach on a Boston Dynamics Spot robot that show our interaction-aware approach achieves higher task success rates and tracking accuracy in pushing objects on stairs compared to open-loop baselines.
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。
