用深度学习让机器人在协作中既安全又高效。
Learning-Based Safety-Aware Task Scheduling for Efficient Human-Robot Collaboration
- 通过学习执行数据，自动建模安全约束对机器人速度的影响。
- 实测显示任务周期时间显著缩短，提升协作效率。
- 无需预设安全规则，适合多种安全逻辑场景应用。
在人机协作中，传统安全措施频繁降低机器人运行速度，影响效率。本文提出一种无需预先知晓安全逻辑的安全感知调度方法，利用深度学习模型，基于执行数据学习系统状态与安全导致的速度降低之间的关系。该框架不显式预测人类动作，而是直接建模交互对机器人速度的影响，简化实现并增强对不同安全逻辑的泛化能力。运行时，学习到的模型优化任务选择以最小化周期时间，同时满足安全要求。在抓取与包装场景的实验中，显著减少了周期时间。
原文摘要 · Abstract (English)
Ensuring human safety in collaborative robotics can compromise efficiency because traditional safety measures increase robot cycle time when human interaction is frequent. This paper proposes a safety-aware approach to mitigate efficiency losses without assuming prior knowledge of safety logic. Using a deep-learning model, the robot learns the relationship between system state and safety-induced speed reductions based on execution data. Our framework does not explicitly predict human motions but directly models the interaction effects on robot speed, simplifying implementation and enhancing generalizability to different safety logics. At runtime, the learned model optimizes task selection to minimize cycle time while adhering to safety requirements. Experiments on a pick-and-packaging scenario demonstrated significant reductions in cycle times.
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