让机器人自适应推动物体导航，无需抓取也能灵活避障
Interactive Navigation with Adaptive Non-prehensile Mobile Manipulation
- 用学习的SE(2)动态模型表征常见物体运动特性
- 在仿真与真实机器人上实现对多种物体的精准推动
- 适合需要动态交互的移动机器人导航任务
本文提出一种基于自适应非抓握式移动操作的交互式导航框架。核心挑战在于处理动态未知的物体，仅靠视觉难以推断其运动特性。为此，我们通过学习的SE(2)动力学表示构建常见室内可移动物体的自适应动力学模型，并将其融入模型预测路径积分（MPPI）控制中，指导机器人交互行为。该模型还用于辅助决策，在无法操控物体时安全绕行。方法在仿真与真实场景中验证，成功部署于动态平衡移动机器人Shmoobot上，在可移动物体导航（NAMO）任务中表现优异。
原文摘要 · Abstract (English)
This paper introduces a framework for interactive navigation through adaptive non-prehensile mobile manipulation. A key challenge in this process is handling objects with unknown dynamics, which are difficult to infer from visual observation. To address this, we propose an adaptive dynamics model for common movable indoor objects via learned SE(2) dynamics representations. This model is integrated into Model Predictive Path Integral (MPPI) control to guide the robot's interactions. Additionally, the learned dynamics help inform decision-making when navigating around objects that cannot be manipulated.Our approach is validated in both simulation and real-world scenarios, demonstrating its ability to accurately represent object dynamics and effectively manipulate various objects. We further highlight its success in the Navigation Among Movable Objects (NAMO) task by deploying the proposed framework on a dynamically balancing mobile robot, Shmoobot. Project website: https://cmushmoobot.github.io/AdaptivePushing/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。