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An Adaptable Robot Vision System Performing Manipulation Actions With Flexible Objects

机译:适应性强的机器人视觉系统,可对柔性物体执行操纵动作

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This paper describes an adaptable system which is able to perform manipulation operations (such as Peg-in-Hole or Laying-Down actions) with flexible objects. As such objects easily change their shape significantly during the execution of an action, traditional strategies, e.g., for solve path-planning problems, are often not applicable. It is therefore required to integrate visual tracking and shape reconstruction with a physical modeling of the materials and their deformations as well as action learning techniques. All these different submodules have been integrated into a demonstration platform, operating in real-time. Simulations have been used to bootstrap the learning of optimal actions, which are subsequently improved through real-world executions. To achieve reproducible results, we demonstrate this for casted silicone test objects of regular shape.
机译:本文介绍了一种适应性强的系统,该系统能够使用灵活的对象执行操纵操作(例如孔中钉或放下动作)。由于这些对象在执行动作期间很容易显着改变其形状,因此,例如用于解决路径规划问题的传统策略通常不适用。因此,需要将视觉跟踪和形状重建与材料及其变形的物理模型以及动作学习技术相结合。所有这些不同的子模块都已集成到一个实时运行的演示平台中。仿真已被用来引导对最佳动作的学习,随后通过实际执行来改进这些动作。为了获得可重现的结果,我们对常规形状的铸造有机硅测试对象进行了演示。

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