Robotic manipulation is essential for modernizing factories and automating industrial tasks like polishing, which require advanced tactile abilities. These robots must be easily set up, safely work with humans, learn tasks autonomously, and transfer skills to similar tasks. Addressing these needs, we introduce the tactile-morph skill framework, which integrates unified force-impedance control with data-driven learning. Our system adjusts robot movements and force application based on estimated energy levels for the desired trajectory and force profile, ensuring safety by stopping if energy allocated for the control runs out. Using a Temporal Convolutional Network, we estimate the energy distribution for a given motion and force profile, enabling skill transfer across different tasks and surfaces. Our approach maintains stability and performance even on unfamiliar geometries with similar friction characteristics, demonstrating improved accuracy, zero-shot transferable performance, and enhanced safety in real-world scenarios. This framework promises to enhance robotic capabilities in industrial settings, making intelligent robots more accessible and valuable.

翻译：机器人操作对于工厂现代化和自动化工业任务（如抛光）至关重要，这些任务需要先进的触觉能力。这些机器人必须易于设置、能够安全地与人类协作、自主地学习任务，并将技能迁移到类似任务中。为满足这些需求，我们提出了触觉形态技能框架，该框架将统一的力-阻抗控制与数据驱动学习相结合。我们的系统根据期望轨迹和力分布所需的估计能量水平来调整机器人运动和施力，并通过在分配给控制的能量耗尽时停止来确保安全性。利用时间卷积网络，我们估计给定运动和力分布的能量分布，从而实现跨不同任务和表面的技能迁移。即使在具有相似摩擦特性的陌生几何体上，我们的方法也能保持稳定性和性能，在实际场景中展现出更高的精度、零样本可迁移性能以及增强的安全性。该框架有望提升机器人在工业环境中的能力，使智能机器人更易获取且更具价值。