用触觉数据训练机器人插孔任务的动态预测模型，提升控制精度。
Learning Contact Dynamics through Touching: Action-conditional Graph Neural Networks for Robotic Peg Insertion
- 基于图神经网络，融合动作条件与接触信息进行动态建模。
- 真实场景中运动预测准确率提升50%，力矩预测精度提高3倍。
- 适合需要高精度触觉反馈的机器人操作任务研究者。
我们提出一种可学习的物理驱动预测模型，用于在接触丰富的操作任务中精确预测机械臂末端的运动及力-扭矩状态。该模型在当前最先进的图神经网络模拟器（FIGNet）基础上引入新型节点与边类型，实现动作条件下的预测，适用于机器人插孔任务中的控制与状态估计。模型通过自监督方式学习，仅需关节编码器和力-扭矩传感器数据，在机器人接触环境时完成训练。仿真结果表明，使用该模型的MPC控制器在复杂插孔任务中性能媲美采用真实动力学模型的控制器；真实实验中，运动预测准确率相比基线提升50%，力-扭矩预测精度提高3倍。最后，我们将模型应用于粒子滤波器中实时追踪机械臂末端，在真实插孔任务中验证了其预测精度的实际应用价值。
原文摘要 · Abstract (English)
We present a learnable physics-based predictive model that provides accurate motion and force-torque prediction of the robot end effector in contact-rich manipulation. The proposed model extends the state-of-the-art GNN-based simulator (FIGNet) with novel node and edge types, enabling action-conditional predictions for control and state estimation in the context of robotic peg insertion. Our model learns in a self-supervised manner, using only joint encoder and force-torque data while the robot is touching the environment. In simulation, the MPC agent using our model matches the performance of the same controller with the ground truth dynamics model in a challenging peg-in-hole task, while in the real-world experiment, our model achieves a 50$\%$ improvement in motion prediction accuracy and 3$\times$ increase in force-torque prediction precision over the baseline physics simulator. Finally, we apply the model to track the robot end effector with a particle filter during real-world peg insertion, demonstrating a practical application of its predictive accuracy.
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