轻量级规划算法，提升机器人在不确定环境中的任务执行效率。
POrTAL: Plan-Orchestrated Tree Assembly for Lookahead
- 融合FF-Replan与POMCP优点，构建前瞻式轻量规划框架
- 计算资源受限下，完成任务所需步数更少，优于基线方法
- 适合中等不确定性场景，适用于实时机器人任务规划
在部分可观测环境中，机器人需在不确定性下高效、稳健地规划以达成任务目标。尽管已有多种概率规划算法，但其在计算资源有限时可能效率低下，或生成的策略执行步数超出预期。为此，我们提出一种新型轻量级概率规划算法——前瞻性计划协同树构建（POrTAL），融合了两种基准算法FF-Replan与POMCP的优势。实验表明，POrTAL是一种任意时间算法，在限定计算时间内，通常能生成比基线更短的最终执行计划，尤其在中等不确定性问题上表现更优。
原文摘要 · Abstract (English)
When tasking robots in partially observable environments, these robots must efficiently and robustly plan to achieve task goals under uncertainty. Although many probabilistic planning algorithms exist for this purpose, these algorithms can be inefficient if executed with the robot's limited computational resources, or may produce policies that take more steps than expected to achieve the goal. We therefore created a new, lightweight, probabilistic planning algorithm, Plan-Orchestrated Tree Assembly for Lookahead (POrTAL), that combines the strengths of two baseline planning algorithms, FF-Replan and POMCP. We demonstrate that POrTAL is an anytime algorithm that generally outperforms these baselines in terms of the final executed plan length given bounded computation time, especially for problems with only moderate levels of uncertainty.
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