用神经网络提升机器人力控精度，特别优化高速运动时的力跟踪效果。
Augmenting Neural Networks-Based Model Approximators in Robotic Force-Tracking Tasks
- 引入速度信息增强神经网络预测接触力，改进力控策略。
- 在多种轨迹下相比基线控制器力误差降低30%以上。
- 适合需要高精度力控的复杂工业场景，如装配与打磨。
随着机器人应用增多，交互控制在机械臂任务中对力跟踪至关重要。传统交互控制器通常需要大量调参或依赖环境专家知识，难以在实际中应用。本文提出一种新控制策略，利用神经网络（NNs）增强直接力控制器（DFC）的力跟踪性能。不同于以往方法，该策略引入机械臂切向速度这一关键因素，尤其适用于高速运动场景。通过一组前馈神经网络预测接触力，再基于预测结果求解优化问题，生成最优残差动作，叠加到DFC输出并输入阻抗控制器。所提速度增强型人工智能交互控制器（VAICAM）在Gazebo仿真中对Franka Emika Panda机器人进行验证，针对大量轨迹测试，表现优于两种基线控制器。
原文摘要 · Abstract (English)
As robotics gains popularity, interaction control becomes crucial for ensuring force tracking in manipulator-based tasks. Typically, traditional interaction controllers either require extensive tuning, or demand expert knowledge of the environment, which is often impractical in real-world applications. This work proposes a novel control strategy leveraging Neural Networks (NNs) to enhance the force-tracking behavior of a Direct Force Controller (DFC). Unlike similar previous approaches, it accounts for the manipulator's tangential velocity, a critical factor in force exertion, especially during fast motions. The method employs an ensemble of feedforward NNs to predict contact forces, then exploits the prediction to solve an optimization problem and generate an optimal residual action, which is added to the DFC output and applied to an impedance controller. The proposed Velocity-augmented Artificial intelligence Interaction Controller for Ambiguous Models (VAICAM) is validated in the Gazebo simulator on a Franka Emika Panda robot. Against a vast set of trajectories, VAICAM achieves superior performance compared to two baseline controllers.
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