仅用一次推挤的末端速度，就能估算物体质量与摩擦系数。
PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers
- 用物理规律指导的Transformer模型，融合牛顿定律和库仑摩擦模型。
- 仿真中误差比带力传感器的基线降低超10%，实测零样本迁移表现更优。
- 适合无传感器机器人在复杂场景中快速获取物理属性，提升操控可靠性。
准确估计物体质量与摩擦系数是可靠机器人操作的基础。尽管交互感知能力强大，但多数方法依赖力/扭矩传感器等专用硬件，限制了可扩展性。本文提出PhyPush，一种基于物理引导的Transformer模型，仅需单次推挤时末端执行器的速度数据（标准机械臂即可获取），即可估计物体质量与摩擦系数。通过在损失函数中融入牛顿第二定律和库仑摩擦模型，该模型提升了物理一致性，并在未见物体与表面下实现良好泛化。在多种设置下，PhyPush在挑战性的域外条件下保持高精度估计。仿真中，其误差较使用特权力数据的基线降低超过10%；真实实验中，从仿真到现实的零样本迁移成功超越纯数据驱动基线。
原文摘要 · Abstract (English)
Accurately estimating object mass and friction is fundamental to reliable robotic manipulation. While interactive perception is powerful, most approaches rely on specialized hardware like force/torque sensors, limiting scalability. This paper introduces PhyPush, a physics-guided Transformer that estimates an object's mass and friction coefficient using only end-effector velocity from a single push, data readily available on standard robotic arms. By incorporating Newton's second law and the Coulomb friction model through a physics-guided loss, the model improves physical consistency and generalizes to unseen objects and surfaces. Across diverse setups, PhyPush consistently achieves highly accurate estimations in challenging out-of-domain conditions. In simulation, it reduces error by over 10% compared to a baseline with privileged force data, while in real-world experiments, it successfully zero-shot transfers from simulation to outperform a purely data-driven baseline.
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