论文标题
移动操作的视觉扎根任务和运动计划
Visually Grounded Task and Motion Planning for Mobile Manipulation
论文作者
论文摘要
任务和运动计划（TAMP）算法旨在帮助机器人实现任务级别的目标，同时保持运动级别的可行性。本文重点介绍涉及需要延长时间的机器人行为（例如，长距离导航）的tamp域。在本文中，我们开发了一种视觉接地方法，以帮助机器人概率地评估动作的可行性，并引入称为Grop的TAMP算法，以优化可行性和效率。我们已经收集了一个数据集，其中包括进行移动操纵任务的机器人的96,000个模拟试验，然后使用数据集学习将符号空间关系扎根以进行行动可行性评估。与竞争性的tamp基线相比，Grop表现出更高的任务完成率，同时保持较低或可比的动作成本。除了在模拟中进行这些广泛的实验外，Grop还在真实的机器人系统上进行了全面实施和测试。
Task and motion planning (TAMP) algorithms aim to help robots achieve task-level goals, while maintaining motion-level feasibility. This paper focuses on TAMP domains that involve robot behaviors that take extended periods of time (e.g., long-distance navigation). In this paper, we develop a visual grounding approach to help robots probabilistically evaluate action feasibility, and introduce a TAMP algorithm, called GROP, that optimizes both feasibility and efficiency. We have collected a dataset that includes 96,000 simulated trials of a robot conducting mobile manipulation tasks, and then used the dataset to learn to ground symbolic spatial relationships for action feasibility evaluation. Compared with competitive TAMP baselines, GROP exhibited a higher task-completion rate while maintaining lower or comparable action costs. In addition to these extensive experiments in simulation, GROP is fully implemented and tested on a real robot system.
