论文标题
学习为机器人任务和运动计划的地面对象
Learning to Ground Objects for Robot Task and Motion Planning
论文作者
论文摘要
已经开发了任务和运动计划（TAMP）算法，以帮助机器人在离散和连续空间中计划行为。机器人面临复杂的现实情况，在该场景中几乎不可能为机器人计划（例如，在厨房或购物中心）建模所有对象或其物理特性。在本文中，我们定义了一个新的以对象为中心的tamp问题，其中tamp机器人不知道对象属性（例如，块的大小和重量）。然后，我们介绍了以对象为中心的任务动作（{\ bf tmoc}），这是一种接地的TAMP算法，该算法通过物理引擎学习了地面对象及其物理性能。 TMOC对于那些涉及动态复杂的机器人 - 摩尔特 - 对象相互作用的任务特别有用，而动态复杂的机器人对象相互作用几乎无法事先建模。我们已经证明和评估了模拟和使用真实机器人的TMOC。结果表明，TMOC的表现优于累积效用文献中的竞争基线。
Task and motion planning (TAMP) algorithms have been developed to help robots plan behaviors in discrete and continuous spaces. Robots face complex real-world scenarios, where it is hardly possible to model all objects or their physical properties for robot planning (e.g., in kitchens or shopping centers). In this paper, we define a new object-centric TAMP problem, where the TAMP robot does not know object properties (e.g., size and weight of blocks). We then introduce Task-Motion Object-Centric planning ({\bf TMOC}), a grounded TAMP algorithm that learns to ground objects and their physical properties with a physics engine. TMOC is particularly useful for those tasks that involve dynamic complex robot-multi-object interactions that can hardly be modeled beforehand. We have demonstrated and evaluated TMOC in simulation and using a real robot. Results show that TMOC outperforms competitive baselines from the literature in cumulative utility.
