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 FastDownward Replanner (FD-Replan) as an efficient task planning method for goal-directed robot reasoning. FD-Replan 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 FD-Replan with current classical planning and belief space planning approaches by solving low entropy goal-directed grocery packing tasks in simulation. FD-Replan shows positive results and promise with respect to planning time, execution time, and task success rate in our simulation experiments.
翻译:在计算概念方面最近取得的进展大大提高了自主机器人进行国家估计和低摄氏度观测的能力。这些进展促使在不确定的情况下重新考虑机器人决策。目前解决连续决策问题的方法表明,它们位于感知的摄氏谱的极端。因此,这些方法要么无法克服感知错误,要么在解决低感知的摄氏度问题方面无实际效率。在考虑低感知时,我们的目标是探索一种更幸福的媒介,将计算效率与我们从现代机器人概念中观察到的不确定形式相平衡。我们建议快速自上而下的重新规划器(FD-REplan)作为目标导向的机器人推理的有效任务规划方法。FD-REplan将信仰空间代表与快速、目标导向的典型规划特征结合起来,以有效规划低感知知性目标导向的推理任务。我们把FD-REplan与当前的典型规划和信任空间规划方法相比较,方法是解决低感感控控目标的现代机器人概念的包装任务。我们建议快速自上向上重新规划,显示积极的结果,并承诺我们进行时间规划的成功试验。