Estimating Value of Assistance for Online POMDP Robotic Agents

Abstract

Robotic agents operating in dynamic, partially observable environments often benefit from teammate assistance. We address the challenge of determining when and how to assist in multi-robot systems where agents can modify the physical environment, such as moving obstacles that block perception or manipulation. For robots using online POMDP planning, evaluating assistance impacts requires computationally intensive policy evaluation, making real-time decisions difficult. We formulate Value of Assistance (VOA) for POMDP agents and develop efficient heuristics that approximate VOA without requiring complete policy evaluation. Our empirical evaluation on both a standard POMDP benchmark and a collaborative manipulation task demonstrates that our Full Information heuristic enables real-time assistance decisions while maintaining sufficient accuracy for effective helping action selection.