This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed, such as a strawberry stem, using a robot tactile finger. The model is integrated into a Deep Functional Predictive Control (d-FPC) system to control the displacement of the stem on the tactile finger during pushes. Pushing an object with a robot finger along a desired trajectory in 3D is a highly nonlinear and complex physical robot interaction, especially when the object is not stably grasped. The proposed approach controls the stem movements on the tactile finger in a prediction horizon. The effectiveness of the proposed FPC is demonstrated in a series of tests involving a real robot pushing a strawberry in a cluster. The results indicate that the d-FPC controller can successfully control PRI in robotic manipulation tasks beyond the handling of strawberries. The proposed approach offers a promising direction for addressing the challenging PRI problem in robotic manipulation tasks. Future work will explore the generalisation of the approach to other objects and tasks.

翻译：本文提出了一种新方法，以解决机器人推动任务中的物理交互问题。该方法利用基于触觉预测的数据驱动前向模型，向控制器提供被推动物体（如草莓茎）在机器人触觉手指上可能的未来运动信息。该模型被集成到深度功能预测控制系统中，以控制推动过程中茎在触觉手指上的位移。用机器手指沿三维空间期望轨迹推动物体是一项高度非线性且复杂的物理交互任务，尤其在物体未被稳定抓取时。所提方法通过在预测时域内控制触觉手指上的茎运动，从而验证其有效性。通过一系列真实机器人在草莓簇中推动草莓的实验，证明了该控制器的性能。结果表明，深度功能预测控制器能够成功控制机器人操作任务中的物理交互，且不局限于草莓处理。所提方法为解决机器人操作任务中具有挑战性的物理交互问题提供了有前景的方向。未来工作将探索该方法对其他物体和任务的泛化能力。