Abstract
In this paper, we address the problem of manipulating multi-particle aggregates using a bimanual robotic system. Our approach enables the autonomous transport of dispersed particles through a series of shaping and pushing actions using robotically-controlled tools. Achieving this advanced manipulation capability presents two key challenges: high-level task planning and trajectory execution. For task planning, we leverage Vision Language Models (VLMs) to enable primitive actions such as tool affordance grasping and non-prehensile particle pushing. For trajectory execution, we represent the evolving particle aggregate's contour using truncated Fourier series, providing efficient parametrization of its closed shape. We adaptively compute trajectory waypoints based on group cohesion and the geometric centroid of the aggregate, accounting for its spatial distribution and collective motion. Through real-world experiments, we demonstrate the effectiveness of our methodology in actively shaping and manipulating multi-particle aggregates while maintaining high system cohesion.
Abstract (translated)
在这篇论文中，我们解决了使用双臂机器人系统操控多颗粒集合体的问题。我们的方法通过一系列的塑形和推动动作，实现了分散粒子的自主运输，这些动作是通过机器人力控工具完成的。实现这种高级操作能力面临两大挑战：高层次任务规划和轨迹执行。对于任务规划，我们利用视觉语言模型（VLMs）来启用基本的操作，如工具抓取能力和非抓握式的颗粒推动。在轨迹执行方面，我们使用截断傅里叶级数表示不断变化的颗粒集合体轮廓，从而高效地对其闭合形状进行参数化。根据群体凝聚性和集合体的几何质心自适应计算路径关键点，考虑其空间分布和集体运动。通过真实世界实验，我们证明了我们的方法在主动塑造和操控多颗粒集合体的同时保持系统高凝聚力的有效性。
URL
https://arxiv.org/abs/2501.13507
