用可解释AI提升机器人抓取安全性，让机器懂工具、知风险。
Generative Grasp Detection and Estimation with Concept Learning-based Safety Criteria
- 基于概念学习提取关键特征，解释模型决策依据
- 在工业场景中验证抓取成功率提升，手递位置更安全
- 适合需要高可靠性的协作机器人应用
神经网络虽具强大拟合能力，但因其复杂性常被视为黑箱，尤其在安全敏感场景中风险显著。为此，我们提出一种协作机器人抓取算法流程：通过视觉检测工具并生成最优抓取姿态。为增强透明度与可靠性，引入可解释AI方法，从输入中提取学习到的特征，并将其与对应类别关联，形成可理解的概念判据，用于确保工作工具的安全处理。本文验证了该方法的一致性及其对交接位置优化的有效性。实验在工业环境中开展，配置相机系统使机器人能准确拾取特定工具与物体。
原文摘要 · Abstract (English)
Neural networks are often regarded as universal equations that can estimate any function. This flexibility, however, comes with the drawback of high complexity, rendering these networks into black box models, which is especially relevant in safety-centric applications. To that end, we propose a pipeline for a collaborative robot (Cobot) grasping algorithm that detects relevant tools and generates the optimal grasp. To increase the transparency and reliability of this approach, we integrate an explainable AI method that provides an explanation for the underlying prediction of a model by extracting the learned features and correlating them to corresponding classes from the input. These concepts are then used as additional criteria to ensure the safe handling of work tools. In this paper, we show the consistency of this approach and the criterion for improving the handover position. This approach was tested in an industrial environment, where a camera system was set up to enable a robot to pick up certain tools and objects.
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