arXiv AI
Jul 1
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
arXiv:2512. 17534v2 Announce Type: replace-cross Abstract: Modeling and controlling fluids is critical across science and engineering.
Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning
arXiv:2607. 13553v1 Announce Type: cross Abstract: Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredictability of realistic environments.
Related stories
arXiv Machine Learning
Jul 1
Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction
arXiv:2606. 31025v1 Announce Type: new Abstract: Active flow control is a fundamental application in engineering.
arXiv Machine Learning
Jun 26
Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries
arXiv:2606. 26154v1 Announce Type: cross Abstract: Autonomous microrobots navigating biological vasculature could enable targeted drug delivery and thrombolysis, yet training control policies for realistic environments remains an open challenge.
arXiv Machine Learning
Jun 9
Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
arXiv:2606. 08513v1 Announce Type: cross Abstract: Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control.
arXiv AI
Jun 9
Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control
arXiv:2606. 08405v1 Announce Type: new Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures.
arXiv Machine Learning
Jul 20
Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems
arXiv:2607. 16177v1 Announce Type: new Abstract: Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems.
arXiv AI
Jul 14
Heuristic Learning for Active Flow Control Using Coding Agents
arXiv:2607. 11565v1 Announce Type: cross Abstract: Active flow control involves nonlinear dynamics, partial observations, and computationally expensive simulations, making controller design particularly challenging.
arXiv Machine Learning
Jul 17
Lyapunov Guidance: A Unified Framework for Stabilizing Generative Flows
arXiv:2607. 14272v1 Announce Type: new Abstract: Flow matching has emerged as an effective framework for learning complex data distributions, but adapting pretrained flow models to new tasks often requires computationally expensive retraining.
arXiv Machine Learning
Jun 11
Multi-agent rendezvous in fluid flows via reinforcement learning
arXiv:2606. 11274v1 Announce Type: cross Abstract: Rendezvous is a critical task for multi-agent systems, requiring agents to coordinate to meet at an unspecified location.
arXiv AI
Jul 16
Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
arXiv:2607. 13579v1 Announce Type: cross Abstract: Enabling quadrupedal robots to traverse complex terrains-from rugged outdoor environments to urban landscapes-requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive locomotion using only onboard sensors.
arXiv AI
Jun 3
AirDreamer: Generalist Drone Navigation with World Models
arXiv:2606. 03252v1 Announce Type: cross Abstract: Navigating a drone in unseen and cluttered environments requires reliable generalization to unseen scene layouts and understanding of environmental structure relative to the robot's capabilities.
arXiv AI
Jul 14
VINE: Taming Generative Control Policies for Reinforcement Learning
arXiv:2607. 10369v1 Announce Type: cross Abstract: Flow-matching policies have emerged as an effective policy parameterization for robot learning.