Abstract
While recent advances in artificial intelligence have achieved human-level performance in environments like Starcraft and Go, many physical reasoning tasks remain challenging for modern algorithms. To date, few algorithms have been evaluated on physical tasks that involve manipulating objects when movable obstacles are present and when tools must be used to perform the manipulation. To promote research on such tasks, we introduce PushWorld, an environment with simplistic physics that requires manipulation planning with both movable obstacles and tools. We provide a benchmark of more than 200 PushWorld puzzles in PDDL and in an OpenAI Gym environment. We evaluate state-of-the-art classical planning and reinforcement learning algorithms on this benchmark, and we find that these baseline results are below human-level performance. We then provide a new classical planning heuristic that solves the most puzzles among the baselines, and although it is 35 times faster than the best baseline planner, it remains below human-level performance.
Abstract (translated)
尽管近年来人工智能技术取得了进展,在星际争霸和围棋等环境中实现了人类水平的表现,但许多物理推理任务对现代算法仍然具有挑战性。迄今为止, few算法已被评估用于涉及当移动障碍物存在时以及必须使用工具进行操纵的任务的物理任务。为了推动此类任务的研究工作,我们引入了PushWorld,这是一个简单物理学环境,需要同时考虑移动障碍物和工具的操纵规划。我们在PDDL和OpenAI gym环境中提供了超过200个PushWorld谜题的基准,并评估了最先进的经典规划和强化学习算法在此基准上的表现。我们发现这些基准结果低于人类水平表现。然后我们提供了一种新的经典规划启发式,解决基准中最难题,尽管它比最好的基准规划器快35倍,但仍低于人类水平表现。
URL
https://arxiv.org/abs/2301.10289
