Inferential decision-making algorithms typically assume that an underlying probabilistic model of decision alternatives and outcomes may be learned a priori or online. Furthermore, when applied to robots in real-world settings they often perform unsatisfactorily or fail to accomplish the necessary tasks because this assumption is violated and/or they experience unanticipated external pressures and constraints. Cognitive studies presented in this and other papers show that humans cope with complex and unknown settings by modulating between near-optimal and satisficing solutions, including heuristics, by leveraging information value of available environmental cues that are possibly redundant. Using the benchmark inferential decision problem known as ``treasure hunt", this paper develops a general approach for investigating and modeling active perception solutions under pressure. By simulating treasure hunt problems in virtual worlds, our approach learns generalizable strategies from high performers that, when applied to robots, allow them to modulate between optimal and heuristic solutions on the basis of external pressures and probabilistic models, if and when available. The result is a suite of active perception algorithms for camera-equipped robots that outperform treasure-hunt solutions obtained via cell decomposition, information roadmap, and information potential algorithms, in both high-fidelity numerical simulations and physical experiments. The effectiveness of the new active perception strategies is demonstrated under a broad range of unanticipated conditions that cause existing algorithms to fail to complete the search for treasures, such as unmodelled time constraints, resource constraints, and adverse weather (fog).

翻译：推断性决策算法通常假设决策方案及结果背后的概率模型可以预先或在线学习。然而，当这类算法被应用于真实环境中的机器人时，往往因该假设不成立，或遭遇未预期的外部压力与约束，而导致性能不佳甚至无法完成必要任务。本文及其他文献中的认知研究表明，人类通过利用环境中可能存在的冗余线索的信息价值，在近最优解与满意解（包括启发式方法）之间进行调节，从而应对复杂未知环境。本文以名为“寻宝”的基准推断决策问题为例，发展了一套通用方法，用于研究并建模压力下的主动感知方案。通过在虚拟世界中模拟寻宝问题，我们的方法从高性能执行者中学习可泛化策略，并将这些策略应用于机器人，使其能够根据外部压力及可用的概率模型（若存在），在最优解与启发式解之间灵活切换。最终形成一套适用于配备摄像头的机器人的主动感知算法。在高保真数值仿真及物理实验中，该算法在寻宝任务上的表现均优于通过细胞分解法、信息路径图法和信息势法获得的方案。在广泛未预期条件（如未建模的时间约束、资源约束及恶劣天气（雾））下，新主动感知策略的有效性也得到了验证——这些条件会导致现有算法无法完成搜索任务。