Memory Centric Power Allocation for Multi-Agent Embodied Question Answering

arXiv cs.RO / 4/21/2026

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Key Points

The paper studies multi-agent embodied question answering (MA-EQA), where robot teams answer questions based on what they have observed over long time horizons.
Instead of optimizing edge resources around sensing, communication, or computation performance, it introduces memory quality as the primary optimization objective.
It proposes a QoM (Quality of Memory) model using forward simulation and generative-adversarial-exam (GAE) to evaluate how well stored information can be retrieved.
Building on the QoM model, it develops memory-centric power allocation (MCPA) that maximizes overall memory quality subject to communication power constraints, allocating more power to robots with higher QoM.
The authors provide asymptotic results linking transmit power to GAE error probability and report extensive experimental gains across multiple scenarios and metrics.

Abstract

This paper considers multi-agent embodied question answering (MA-EQA), which aims to query robot teams on what they have seen over a long horizon. In contrast to existing edge resource management methods that emphasize sensing, communication, or computation performance metrics, MA-EQA emphasizes the memory qualities. To cope with this paradigm shift, we propose a quality of memory (QoM) model based on generative adversarial exam (GAE), which leverages forward simulation to assess memory retrieval and uses the resulting exam scores to compute QoM values. Then we propose memory centric power allocation (MCPA), which maximizes the QoM function under communication resource constraints. Through asymptotic analysis, it is found that the transmit powers are proportional to the GAE error probability, thus prioritizing towards high-QoM robots. Extensive experiments demonstrate that MCPA achieves significant improvements over extensive benchmarks in terms of diverse metrics in various scenarios.