arXiv Machine Learning
Asynchronous Cooperative Online Learning for Multi-Robot Control under Computational Delays
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arXiv Machine Learning
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Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency
The paper introduces ARLI, a latency‑aware framework that enables reinforcement learning fine‑tuning of large generalist robot policies despite inference delays. ARLI combines asynchronous inference with state augmentations—incorporating committed actions and mid‑inference observations—to restore near‑Markovian dynamics and maintain reactivity. Experiments on simulated and real‑world manipulation tasks show that ARLI allows effective policy improvement under latency, outperforming standard RL even in no‑latency scenarios.
arXiv Machine Learning
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Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots
arXiv:2609.14567v1 Announce Type: cross Abstract: Reinforcement learning (RL) has shown considerable promise for robotic decision-making, yet deploying multi-agent RL (MARL) on physical multi-robot s...
arXiv Machine Learning
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Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies
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arXiv Machine Learning
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Distributed GNEP Algorithms without Multiplier Sharing and Applications to Multi-Robot Coordination and Contextual Bandit-Based Active Learning
arXiv:2606. 00759v1 Announce Type: new Abstract: Recent advances in artificial intelligence have expanded the focus from classical optimization to include equilibrium analysis in noncooperative games.
arXiv AI
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HALO: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization
arXiv:2603. 03741v2 Announce Type: replace-cross Abstract: To improve generalization and resilience in human-robot collaboration (HRC), robots must contend with diverse combinations of human behaviors and contexts, motivating multi-agent reinforcement learning (MARL).
arXiv AI
Jun 17
ED3R: Energy-Aware Distributed Disaster Detection Enabled by Cooperative Robotic Agents
arXiv:2606. 17739v1 Announce Type: cross Abstract: Robotics are expected to support environmental monitoring and natural disaster management, where decisions must be made under uncertainty, resource limitations, and strict operational constraints.