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.

翻译：随着机器人技术的日益普及，交互控制在确保基于机械臂的任务中的力跟踪方面变得至关重要。通常，传统的交互控制器要么需要进行大量调参，要么要求具备环境相关的专家知识，这在实际应用中往往不切实际。本研究提出了一种新颖的控制策略，利用神经网络来增强直接力控制器的力跟踪性能。与以往类似方法不同，该策略考虑了机械臂的切向速度——这是施加力的关键因素，尤其是在快速运动过程中。该方法采用前馈神经网络集成来预测接触力，然后利用该预测求解一个优化问题，生成最优残差动作；该残差动作被添加到直接力控制器的输出中，并施加于一个阻抗控制器。所提出的速度增强型模糊模型人工智能交互控制器在Gazebo仿真环境中使用Franka Emika Panda机器人进行了验证。在大量轨迹测试中，VAICAM相比两个基线控制器均取得了更优的性能。