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，与机器人直接物理交互正变得日益重要，但无防护栏的机器人同样给操作者带来更大风险。为降低潜在风险，现行操作规范要求采取较为简单的措施，例如在发生物理接触或安全距离被违反时停止机器人运动。尽管这种方法能基本避免人体伤害，但所有此类解决方案的共同缺陷在于：人机之间难以实现真正协作，因此无法充分发挥此类系统的工作优势。在人机协作场景中，需要更复杂的解决方案，使机器人能够根据操作者特征和/或当前情境调整行为。最关键的是，在机器人自由运动过程中，必须允许为有意义的交互而发生的物理接触，且不应将其识别为碰撞。然而，这恰恰是未来系统的核心挑战：如何利用机器人本体感知与机器学习算法检测人类接触。本研究采用深度度量学习方法，区分无接触机器人运动、旨在实现物理人机交互的有意接触以及碰撞情境。取得的成果令人振奋：DML方法实现了98.6%的分类准确率，较现有标准（即未采用DML训练的深度学习网络）提升4%。研究还表明该方法展现出优异的泛化能力，可在无需使用目标机器人数据重新训练模型的情况下，通过接触检测（区分无接触、有意接触与非故意接触）便捷地迁移至其他机器人平台。