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.

翻译：我们提出了一种可学习的基于物理的预测模型，该模型能够准确预测接触密集型操作中机器人末端执行器的运动及力/力矩。所提出的模型通过引入新型节点与边类型，对当前最先进的基于图神经网络（GNN）的模拟器（FIGNet）进行了扩展，使其能够在机器人轴孔装配场景中实现面向控制与状态估计的动作条件预测。我们的模型以自监督方式学习，仅利用机器人与环境接触时的关节编码器数据及力/力矩数据。在仿真环境中，使用本模型的模型预测控制（MPC）智能体在具有挑战性的轴孔装配任务中，达到了与采用真实动力学模型的相同控制器相当的性能；而在真实世界实验中，本模型相较于基准物理模拟器，运动预测精度提升了50%，力/力矩预测精度提高了3倍。最后，我们将该模型应用于真实世界轴孔装配过程中基于粒子滤波器的机器人末端执行器跟踪，展示了其预测精度在实际应用中的价值。