论文标题
大象不打包杂货：低熵信念的机器人任务计划
Elephants Don't Pack Groceries: Robot Task Planning for Low Entropy Belief States
论文作者
论文摘要
计算感知的最新进展显着提高了自主机器人以低熵进行状态估计的能力。这种进步激发了在不确定性下重新考虑机器人决策的重新考虑。当前解决顺序决策问题的方法模型为居住在感知熵谱的极端情况下。因此，这些方法要么无法克服感知误差，要么渐近地解决低感知熵的问题。考虑到低熵的感知，我们的目标是探索一种更快乐的媒介，以平衡计算效率与现在从现代机器人感知中观察到的不确定性形式。我们提出了一种用于目标指导机器人推理的有效任务计划的方法。我们的方法将信仰空间表示与经典计划的快速，目标指导的特征相结合，以有效地计划低熵目标定向的推理任务。我们通过在模拟中解决低熵导向的杂货包装任务来将我们的方法与当前的经典计划和信念空间规划方法进行比较。在我们的模拟实验中，我们的方法在计划时间，执行时间和任务成功率方面的表现优于这些方法。我们还通过物理机器人展示了对现实世界杂货包装任务的方法。
Recent advances in computational perception have significantly improved the ability of autonomous robots to perform state estimation with low entropy. Such advances motivate a reconsideration of robot decision-making under uncertainty. Current approaches to solving sequential decision-making problems model states as inhabiting the extremes of the perceptual entropy spectrum. As such, these methods are either incapable of overcoming perceptual errors or asymptotically inefficient in solving problems with low perceptual entropy. With low entropy perception in mind, we aim to explore a happier medium that balances computational efficiency with the forms of uncertainty we now observe from modern robot perception. We propose an approach for efficient task planning for goal-directed robot reasoning. Our approach combines belief space representation with the fast, goal-directed features of classical planning to efficiently plan for low entropy goal-directed reasoning tasks. We compare our approach with current classical planning and belief space planning approaches by solving low entropy goal-directed grocery packing tasks in simulation. Our approach outperforms these approaches in planning time, execution time, and task success rate in our simulation experiments. We also demonstrate our approach on a real world grocery packing task with physical robot.
