任意时刻概率约束可证明收敛的在线信念空间规划
Anytime Probabilistically Constrained Provably Convergent Online Belief Space Planning
AI总结：
本文提出一种在连续域中基于MCTS的任意时刻概率约束在线信念空间规划方法，在搜索未收敛时也能保证安全，理论证明指数速率概率收敛，仿真表明其在极低树查询下仍优于基线。
摘要：
考虑未来风险对于自主运行的机器人在线寻找不仅最佳而且安全的执行动作至关重要。本文基于近期提出的概率信念相关约束公式。我们提出了一种在连续域中采用蒙特卡洛树搜索（MCTS）方法的任意时刻方法。与以往方法不同，我们的方法在任意时刻都能保证相对于当前扩展搜索树的安全性，且不依赖于搜索的收敛。我们证明了算法的一个版本以指数速率在概率上收敛，并通过大量仿真研究了所提出的技术。即使只有极少量的树查询，我们方法找到的最佳动作也比基线安全得多。此外，在目标方面，我们的方法不断找到比基线更好的动作。这是因为我们修改了搜索树中维护的值和统计数据，并从中移除了被剪枝动作的贡献。
英文摘要：
Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon the recently introduced formulation of probabilistic belief-dependent constraints. We present an anytime approach employing the Monte Carlo Tree Search (MCTS) method in continuous domains. Unlike previous approaches, our method assures safety anytime with respect to the currently expanded search tree without relying on the convergence of the search. We prove convergence in probability with an exponential rate of a version of our algorithms and study proposed techniques via extensive simulations. Even with a tiny number of tree queries, the best action found by our approach is much safer than the baseline. Moreover, our approach constantly finds better than the baseline action in terms of objective. This is because we revise the values and statistics maintained in the search tree and remove from them the contribution of the pruned actions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
