Everyday robotics are challenged to deal with autonomous product handling in applications such as logistics or retail, possibly causing damage to the items during manipulation. Traditionally, most approaches try to minimize physical interaction with goods. However, this paper proposes to take into account any unintended object motion and to learn damage-minimizing manipulation strategies in a self-supervised way. The presented approach consists of a simulation-based planning method for an optimal manipulation sequence with respect to possible damage. The planned manipulation sequences are generalized to new, unseen scenes in the same application scenario using machine learning. This learned manipulation strategy is continuously refined in a self-supervised, simulation-in-the-loop optimization cycle during load-free times of the system, commonly known as mental simulation. In parallel, the generated manipulation strategies can be deployed in near-real time in an anytime fashion. The approach is validated on an industrial container-unloading scenario and on a retail shelf-replenishment scenario.

中文翻译：

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通过心理模拟对自我监督的避免伤害的操纵策略进行优化

每天，机器人技术都面临着在物流或零售等应用中处理自主产品处理的挑战，这可能会在操作过程中损坏物品。传统上，大多数方法都试图将与商品的物理交互最小化。然而，本文提出要考虑到任何意外的物体运动，并以自我监督的方式学习最小化损伤的操纵策略。提出的方法包括一个基于仿真的计划方法，该方法针对可能的损坏采取了最佳的操作顺序。使用机器学习，将计划的操作序列推广到同一应用场景中新的看不见的场景。在系统无负载的时候，这种自学的操纵策略会在自我监督的仿真循环优化周期中不断完善，心理模拟。并行地，生成的操纵策略可以随时随地近乎实时地部署。该方法在工业集装箱卸载方案和零售货架补充方案中得到了验证。