Object rearrangement is widely demanded in many of the manipulation tasks performed by industrial and service robots. Rearranging an object through planar pushing is deemed energy efficient and safer compared with the pick-and-place operation. However, due to the unknown physical properties of the object, rearranging an object toward the target position is difficult to accomplish. Even though robots can benefit from multimodal sensory data for estimating novel object dynamics, the exact estimation error bound is still unknown. In this work, first, we demonstrate a way to obtain an error bound on the center of mass (CoM) estimation for the novel object only using a position-controlled robot arm and a vision sensor. Specifically, we extend Mason's Voting Theorem to object CoM estimation in the absence of accurate information on friction and object shape. The probable CoM locations are monotonously narrowed down to a convex region, and the extended voting theorems' guarantee that the convex region contains the CoM ground truth in the presence of contact normal estimation error and pushing execution error. For the object translation task, existing methods generally assume that the pusher-object system's physical properties and full-state feedback are available, or utilize iterative pushing executions, which limits the application of planar pushing to real-world settings. In this work, assuming a nominal friction coefficient between the pusher and object through contact normal error bound analysis, we leverage the estimated convex region and the Zero Moment Two Edge Pushing method (Gao et al., 2023) to select the contact configurations for object pure translation. It is ensured that the selected contact configurations are capable of tolerating the CoM estimation error. The experimental results show that the object can be accurately translated to the target position with only two controlled pushes at most.

中文翻译：

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梅森投票定理在纯平移运动质心估计中的通用性及应用

工业和服务机器人执行的许多操作任务中广泛需要对象重新排列。与拾放操作相比，通过平面推动重新排列物体被认为是节能且安全的。然而，由于物体的物理特性未知，将物体重新排列到目标位置是很难实现的。尽管机器人可以受益于多模态传感数据来估计新颖的物体动力学，但确切的估计误差范围仍然未知。在这项工作中，首先，我们演示了一种仅使用位置控制机器人臂和视觉传感器来获得新物体质心 (CoM) 估计误差范围的方法。具体来说，我们将梅森投票定理扩展到缺乏摩擦力和物体形状的准确信息的情况下的物体 CoM 估计。可能的 CoM 位置单调缩小到凸区域，并且扩展投票定理保证凸区域在存在接触法线估计误差和推动执行误差的情况下包含 CoM 基本事实。对于对象翻译任务，现有方法通常假设推送对象系统的物理属性和全状态反馈可用，或者利用迭代推送执行，这限制了平面推送在现实世界设置中的应用。在这项工作中，通过接触法线误差界限分析假设推动器和物体之间的标称摩擦系数，我们利用估计的凸区域和零力矩双边缘推动方法（Gao 等人，2023）来选择物体的接触配置纯翻译。确保所选接触配置能够承受 CoM 估计误差。实验结果表明，最多只需两次受控推动即可将物体准确平移到目标位置。