Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects
Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects
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DOI：
10.1109/icra48891.2023.10160306
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Aidan Curtis;L. Kaelbling;Siddarth Jain
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文献类型：
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作者：
Aidan Curtis;L. Kaelbling;Siddarth Jain
表示和推理的不确定性是至关重要的自治代理在部分可观察的环境中与噪声传感器。部分可观测马尔可夫决策过程（POMDPs）作为一个通用的框架，代表问题中的不确定性是一个重要因素。基于在线样本的POMDP方法已经成为解决大型POMDP的有效方法，并已被证明可以扩展到连续域。然而，这些解决方案很难在具有显著不确定性的问题中找到长期计划。探索地理学可以帮助指导规划，但许多现实世界的设置包含重大的任务无关的不确定性，可能会分散任务目标。在本文中，我们提出了一种在线POMDP求解器，能够处理需要长期规划的领域，具有显着的任务相关和任务无关的不确定性。我们展示了我们的解决方案在几个时间上的扩展版本的玩具POMDP问题，以及机器人操作的铰接对象使用神经感知前端构建一个可能的模型分布。我们的研究结果表明，BLOG优于目前基于样本的在线POMDP求解器在几个任务。
Representing and reasoning about uncertainty is crucial for autonomous agents acting in partially observable environments with noisy sensors. Partially observable Markov decision processes (POMDPs) serve as a general framework for representing problems in which uncertainty is an important factor. Online sample-based POMDP methods have emerged as efficient approaches to solving large POMDPs and have been shown to extend to continuous domains. However, these solutions struggle to find long-horizon plans in problems with significant uncertainty. Exploration heuristics can help guide planning, but many real-world settings contain significant task-irrelevant uncertainty that might distract from the task objective. In this paper, we propose STRUG, an online POMDP solver capable of handling domains that require long-horizon planning with significant task-relevant and task-irrelevant uncertainty. We demonstrate our solution on several temporally extended versions of toy POMDP problems as well as robotic manipulation of articulated objects using a neural perception frontend to construct a distribution of possible models. Our results show that STRUG outperforms the current sample-based online POMDP solvers on several tasks.