aiqbench analysis
This research applies LSTM-based encoder-decoder networks to semantically compress data in distributed model prediction control, significantly reducing communication loads for multi-agent robotic systems without sacrificing performance. By reconstructing full messages from reduced representations, the approach addresses bandwidth bottlenecks inherent in real-time coordination tasks where agents must exchange information at every time step. The method demonstrates that semantic encoding can reliably maintain system stability even under conditions that would overwhelm standard wireless technologies with uncompressed data streams.
Communication Reduction via Semantic-Based Encoding in DMPC Using LSTMs
arXiv:2608.17592v1 Announce Type: cross Abstract: The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time step. To semantically reduce communication demands, this work employs encoder-decoder networks built around long-short term memory (LSTM) cells in a distributed optimization algorithm. Agents publish a reduced representation of a message and receivers reconstruct the original message upon reception. In tests with reduced communication using formations of mobile robots, trained networks retain satisfactory performance and work reliably under conditions overwhelming full communication. As the results show, the usage of LSTMs either allows unprecedented reconstruction accuracy or the usage of different prediction-horizon lengths without the necessity to retrain.
Abstract:The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time step. To semantically reduce communication demands, this work employs encoder-decoder networks built around long-short term memory (LSTM) cells in a distributed optimization algorithm. Agents publish a reduced representation of a message and receivers reconstruct the original message upon reception. In tests with reduced communication using formations of mobile robots, trained networks retain satisfactory performance and work reliably under conditions overwhelming full communication. As the results show, the usage of LSTMs either allows unprecedented reconstruction accuracy or the usage of different prediction-horizon lengths without the necessity to retrain.
Systems and Control (eess.SY); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Robotics (cs.RO)
arXiv:2608.17592 [eess.SY]
(or arXiv:2608.17592v1 [eess.SY] for this version)
https://doi.org/10.48550/arXiv.2608.17592 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 150 words
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