仅用机器人自身感知，几秒内推断物体质量与弹性模量。
Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction
- 通过分析机器人动作反应，反推物体物理属性。
- 在低成本机器人上实现物体质量与弹性模量精准估计。
- 无需视觉或外接传感器，适用于任意机械臂。
可微分仿真已成为系统识别的强大工具。以往研究多基于机器人自身数据识别机器人属性，或基于物体自身数据识别物体属性。本文提出一种新方法：仅利用机器人本体感知信息（如关节编码器数据），在不依赖物体特定数据的情况下，反向标定物体属性。核心观察是：通过分析机器人对物体操作时的反应，可推断出物体的惯性、软硬度等特性。我们构建了可微分的机器人-物体交互仿真模型，实现物体属性的逆向估计。该方法完全依赖机器人本体感知，无需外部测量设备或视觉追踪系统。适用于任何串联机器人，仅需关节位置信息即可。我们在低成本机器人平台上验证了方法的有效性，仅用笔记本电脑几秒计算即完成物体质量与弹性模量的准确估计。
原文摘要 · Abstract (English)
Differentiable simulation has become a powerful tool for system identification. While prior work has focused on identifying robot properties using robot-specific data or object properties using object-specific data, our approach calibrates object properties by using information from the robot, without relying on data from the object itself. Specifically, we utilize robot joint encoder information, which is commonly available in standard robotic systems. Our key observation is that by analyzing the robot's reactions to manipulated objects, we can infer properties of those objects, such as inertia and softness. Leveraging this insight, we develop differentiable simulations of robot-object interactions to inversely identify the properties of the manipulated objects. Our approach relies solely on proprioception -- the robot's internal sensing capabilities -- and does not require external measurement tools or vision-based tracking systems. This general method is applicable to any articulated robot and requires only joint position information. We demonstrate the effectiveness of our method on a low-cost robotic platform, achieving accurate mass and elastic modulus estimations of manipulated objects with just a few seconds of computation on a laptop.
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