Direct physical interaction with robots is becoming increasingly important in flexible production scenarios, but robots without protective fences also pose a greater risk to the operator. In order to keep the risk potential low, relatively simple measures are prescribed for operation, such as stopping the robot if there is physical contact or if a safety distance is violated. Although human injuries can be largely avoided in this way, all such solutions have in common that real cooperation between humans and robots is hardly possible and therefore the advantages of working with such systems cannot develop its full potential. In human-robot collaboration scenarios, more sophisticated solutions are required that make it possible to adapt the robot's behavior to the operator and/or the current situation. Most importantly, during free robot movement, physical contact must be allowed for meaningful interaction and not recognized as a collision. However, here lies a key challenge for future systems: detecting human contact by using robot proprioception and machine learning algorithms. This work uses the Deep Metric Learning (DML) approach to distinguish between non-contact robot movement, intentional contact aimed at physical human-robot interaction, and collision situations. The achieved results are promising and show show that DML achieves 98.6\% accuracy, which is 4\% higher than the existing standards (i.e. a deep learning network trained without DML). It also indicates a promising generalization capability for easy portability to other robots (target robots) by detecting contact (distinguishing between contactless and intentional or accidental contact) without having to retrain the model with target robot data.

翻译：在柔性生产场景中，与机器人的直接物理交互日益重要，但无防护围栏的机器人也给操作员带来更大风险。为降低潜在风险，当前采用相对简单的操作规范，例如在发生物理接触或违反安全距离时使机器人停止运行。虽然这种方式能大幅避免人体伤害，但所有此类解决方案的共同缺陷在于：真正的人机协作几乎无法实现，因此人机协作系统的优势难以充分发挥。在人机协作场景中，需要更精密的解决方案，使机器人能够根据操作员和/或当前情境调整行为。关键在于，在机器人自由运动过程中，必须允许有意义的物理接触——这类接触不应被识别为碰撞。这正是未来系统面临的核心挑战：利用机器人本体感知能力与机器学习算法检测人体接触。本研究采用深度度量学习（DML）方法，区分非接触式机器人运动、面向物理人机交互的有意接触与碰撞场景。实验结果表明，DML方法实现了98.6%的准确率，较现有标准（即未经DML训练的深度学习网络）提升4%。同时，该方法展现出优异的泛化能力：无需使用目标机器人数据进行重训练，即可通过接触检测（区分非接触、有意接触与意外接触）便捷迁移至其他机器人（目标机器人）。