Online Active Perception for Partially Observable Markov Decision Processes with Limited Budget

TL;DR

This paper introduces a generalized greedy active perception strategy for partially observable Markov decision processes, enabling agents to efficiently gather information under limited budgets with near-optimal performance guarantees.

Contribution

It presents a novel greedy approach with theoretical guarantees for active perception in POMDPs under budget constraints, adaptable to runtime decision-making.

Findings

Strategy achieves near-optimal uncertainty reduction

Demonstrated effectiveness in robotic navigation simulations

Provides theoretical performance guarantees

Abstract

Active perception strategies enable an agent to selectively gather information in a way to improve its performance. In applications in which the agent does not have prior knowledge about the available information sources, it is crucial to synthesize active perception strategies at runtime. We consider a setting in which at runtime an agent is capable of gathering information under a limited budget. We pose the problem in the context of partially observable Markov decision processes. We propose a generalized greedy strategy that selects a subset of information sources with near-optimality guarantees on uncertainty reduction. Our theoretical analysis establishes that the proposed active perception strategy achieves near-optimal performance in terms of expected cumulative reward. We demonstrate the resulting strategies in simulations on a robotic navigation problem.