用可微接触动力学实现不确定环境下稳定抓取与放置
Differentiable Contact Dynamics for Stable Object Placement Under Geometric Uncertainties
- 通过可微仿真建模接触力，反推几何不确定性
- 在Franka机械臂上对多种不确定场景有效提升放置稳定性
- 融合信念更新机制，增强对初始值的鲁棒性
从端咖啡到装配零件，稳定物体放置是未来机器人的重要能力。当物体姿态或形状存在几何不确定性时，该任务尤为困难。本文利用可微接触动力学仿真模型，推导出力矩传感器读数与几何不确定性之间的新梯度，从而通过梯度下降最小化传感器数据与模型预测间的差异，实现不确定性估计。由于梯度方法对初始化敏感，我们采用多估计信念分布，并在每个时间步基于当前信念选择机器人动作。在Franka机械臂上的实验表明，该方法在多种几何不确定性下（包括抓握物体的位姿不确定性、物体形状不确定性及环境不确定性）均取得了良好表现。
原文摘要 · Abstract (English)
From serving a cup of coffee to positioning mechanical parts during assembly, stable object placement is a crucial skill for future robots. It becomes particularly challenging under geometric uncertainties, e.g., when the object pose or shape is not known accurately. This work leverages a differentiable simulation model of contact dynamics to tackle this challenge. We derive a novel gradient that relates force-torque sensor readings to geometric uncertainties, thus enabling uncertainty estimation by minimizing discrepancies between sensor data and model predictions via gradient descent. Gradient-based methods are sensitive to initialization. To mitigate this effect, we maintain a belief over multiple estimates and choose the robot action based on the current belief at each timestep. In experiments on a Franka robot arm, our method achieved promising results on multiple objects under various geometric uncertainties, including the in-hand pose uncertainty of a grasped object, the object shape uncertainty, and the environment uncertainty.
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