arXiv Machine Learning
Aug 26

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.

By Brian Zhu (Siemens), Momen Khalil (Siemens), E Harrison (UC Berkeley), Emanuele Poggi (Siemens), Philipp Schmitt (Siemens), Bernd Kast (Siemens), Philine Meister (Siemens), Pranav Atreya (UC Berkeley), Qiyang Li (UC Berkeley), Finn Ferchau (Siemens), Cesar Colmenero (Siemens), Yash Shahapurkar (Siemens), Gokul Narayanan (Siemens), Melih Erdogan (Siemens), Kai Wurm (Siemens), Georg von Wichert (Siemens), Oier Mees (Microsoft, ETH Zurich, UC Berkeley), Eugen Solowjow (Siemens), Andrew Wagenmaker (UC Berkeley), Sergey Levine (UC Berkeley)
arXiv Machine Learning
Jun 5

Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies

arXiv:2606. 06011v1 Announce Type: cross Abstract: In this work, we propose a framework that combines multi-agent reinforcement learning (MARL) with model-based control to achieve safe, dynamically feasible actions in cooperative multi-agent tasks.

By Christian Llanes, Spencer W. Jensen, Samuel Coogan
arXiv Machine Learning
Oct 19

Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic Environment

The paper proposes a centralized training with decentralized execution framework for multi‑agent UAV pathfinding in stochastic environments, leveraging multi‑agent reinforcement learning enhanced by model predictive control concepts. Agents communicate only with a centralized planner and use a distance‑weighted mean‑field observation to plan feasible paths, while multi‑step value convergence improves training efficiency. Experiments, including ablation and real‑robot studies, demonstrate the method’s effectiveness and generalization.

By Qizhen Wu, Kexin Liu, Lei Chen, Jinhu L\"u
arXiv Machine Learning
Oct 16

Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning

The paper introduces A‑SLS, a learning‑aware robust adaptive model predictive control framework that forecasts future parameter uncertainty and preserves correlations among disturbances, parameter errors, and estimate updates to build tight reachable sets. It presents a GPU‑accelerated solver enabling real‑time performance on high‑dimensional systems, demonstrated on a quadruped with 61 states and a Crazyflie drone that learns dynamics online to stabilize during flight.

By Anutam Srinivasan, Siddhant Panse, Glen Chou