用声音感知学习动态摩擦，让机器人运东西更稳更快。
Hearing the Slide: Acoustic-Guided Constraint Learning for Fast Non-Prehensile Transport
- 通过声音传感器捕捉振动，实时学习运动中的摩擦系数
- 相比传统模型，物体位移减少86.0%，显著提升运输稳定性
- 适合需要高速、多物并行运输的工业场景
物体搬运是机器人自动化的核心任务，高效安全的搬运方法至关重要。非抓取式搬运可同时处理多个物体，适用于不适合平行夹持或吸力抓取的物体，从而提升效率。现有方法依赖库仑摩擦模型施加约束，但在高速运动中因机械振动导致建模不准确，易引发物体滑动甚至掉落。为此，本文提出一种基于声学感知的新方法，通过将托盘运动轨迹映射为动态调节的摩擦系数，学习真实环境下的摩擦特性。该模型被集成至优化型运动规划器，在每个控制步根据当前规划动作动态调整摩擦约束。在UR5e机器人上对多种物体进行时间最优轨迹生成实验，对比标准库仑模型与所提学习模型的表现。结果表明，新模型使物体位移最多减少86.0%，验证了声学感知在学习实际摩擦约束方面的有效性。
原文摘要 · Abstract (English)
Object transport tasks are fundamental in robotic automation, emphasizing the importance of efficient and secure methods for moving objects. Non-prehensile transport can significantly improve transport efficiency, as it enables handling multiple objects simultaneously and accommodating objects unsuitable for parallel-jaw or suction grasps. Existing approaches incorporate constraints based on the Coulomb friction model, which is imprecise during fast motions where inherent mechanical vibrations occur. Imprecise constraints can cause transported objects to slide or even fall off the tray. To address this limitation, we propose a novel method to learn a friction model using acoustic sensing that maps a tray's motion profile to a dynamically conditioned friction coefficient. This learned model enables an optimization-based motion planner to adjust the friction constraint at each control step according to the planned motion at that step. In experiments, we generate time-optimized trajectories for a UR5e robot to transport various objects with constraints using both the standard Coulomb friction model and the learned friction model. Results suggest that the learned friction model reduces object displacement by up to 86.0% compared to the baseline, highlighting the effectiveness of acoustic sensing in learning real-world friction constraints.
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