Memory Centric Power Allocation for Multi-Agent Embodied Question Answering
For robot teams performing long-horizon question answering, this work introduces a memory-centric resource allocation paradigm, though the improvements are incremental over existing methods.
This paper tackles multi-agent embodied question answering (MA-EQA) by proposing a quality of memory (QoM) model and a memory centric power allocation (MCPA) method. MCPA achieves significant improvements over benchmarks in diverse metrics across various scenarios.
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.