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Asynchronous Cooperative Online Learning for Multi-Robot Control under Computational Delays

Paper recorded by Signals 4 on 2026-08-30 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.LG · 机器学习 · first seen 2026-09-01

Abstract

Ensuring the safe operation of multi-agent systems (MASs) under uncertain environments is crucial for cooperative robotic, where external disturbances and inaccurate dynamic models can significantly compromise performance and reliability. To address this challenge, calibrated machine learning models, particularly Gaussian process (GP) regression, are extensively employed due to their interpretable

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#195 most recent of 215 cs.LG papers we have recorded · ↑ newer: TACS: Trajectory-Aware Candidate Selection for LLM Jailbreak Suffix Op · ↓ older: Which LLM for Which Work? Budgeted Model Allocation under Uncertain Ev
Cite this page: Asynchronous Cooperative Online Learning for Multi-Robot Control under Computational Delays: the #195 most recent of 215 cs.LG papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/asynchronous-cooperative-online-learning-for-multi-robot-control-under-computati.html
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