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We study the problem of objects search in clutter. In cluttered environments, partial occlusion among objects prevents vision systems from correctly recognizing objects. Hence, the agent needs to move objects around to gather information, which helps reduce uncertainty in perception. At the same time, the agent needs to minimize the efforts of moving objects to reduce the time required to complete the task. We model the problem as a Partially Observable Markov Decision Process (POMDP), formulating it as a problem of optimal decision making under uncertainty. By exploiting spatial constraints, we are able to adapt online POMDP planners to handle objects search problems with large state space and action space. Experiments show that the POMDP solution outperforms greedy approaches, especially in cases where multi-step manipulation is required.
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Li et al. (2016) studied this question.