Heuristic Satisficing Inferential Decision Making in Human and Robot Active Perception

TL;DR

This paper introduces a heuristic satisficing approach for inferential decision-making in robots, inspired by human strategies, enabling better performance under unpredictable real-world conditions through active perception algorithms tested in virtual and physical treasure hunt scenarios.

Contribution

It develops a generalizable active perception framework that allows robots to switch between optimal and heuristic solutions based on external pressures and environmental cues.

Findings

Robots outperform existing methods in treasure hunt tasks.

Strategies are effective under unanticipated conditions like fog and resource constraints.

Approach learns from high-performing human strategies in virtual simulations.

Abstract

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).