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.
Autonomous Droplet Navigation via Model-Based Reinforcement Learning
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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 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 16
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.
arXiv AI
Aug 26
Self-Evolving Scientific Agent Designs Physically-Reasoned Whitebox Fluid Control
The paper introduces a self‑evolving scientific agent that uses large language models and iterative code generation to build interpretable, physically‑reasoned white‑box controllers for complex systems. The agent deploys candidate controllers in simulations, diagnoses dynamic behavior from multimodal evidence, and refines source code until a robust policy is achieved. Applied to a nonlinear fluid‑structure interaction problem—a two‑joint dogfish swimmer navigating an unsteady wake—the agent autonomously designs a controller that consistently reaches targets across a wide range of conditions without retraining.
arXiv AI
Sep 18
Accelerating Visual Policy Learning with Sampling-Based Model Predictive Control
The paper introduces Sampling-Guided Policy Search (SGPS), a method that combines sampling-based model‑predictive control with first‑order policy gradients to accelerate visual policy learning for locomotion and manipulation tasks. SGPS starts with behavior cloning from sampled actions and then alternates between sampling‑based refinement and short‑horizon policy updates under varied initial states and dynamics. The approach is demonstrated on simulated Unitree Go2 and G1 robots, learning tasks such as obstacle traversal and bimanual carrying, and the distilled policies transfer zero‑shot to a real Go2 robot using onboard depth perception.
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 AI
Sep 11
Self-Evolving Scientific Agent Designs Physically Reasoned White-Box Fluid Control
The paper presents a method where self‑evolving scientific agents design explicit, neural‑network‑free white‑box controllers for fluid dynamics tasks. By iteratively interpreting simulation data, the agents accumulate control knowledge and refine controller code, ultimately achieving robust control of an underactuated two‑joint swimmer in unsteady flows. The resulting controllers generalize across varying target positions, wake geometries, cylinder counts, and inflow speeds, and 2D control priors successfully transfer to accelerate 3D adaptation.
arXiv Machine Learning
Sep 15
VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching
arXiv:2609.14261v1 Announce Type: cross Abstract: Recent robot learning paradigms increasingly rely on large offline datasets of robotic interactions to train control policies. Expressive generative...
arXiv Computer Vision
Sep 18
Learning Foresight without Explicit Trajectories for 3D Diffusion Policies
The paper introduces Movement Trend Guidance, a method that equips 3D diffusion policies with foresight by learning a compact latent representation of interaction evolution from a brief observation history. This latent, supervised by sparse future gripper states during training, serves as future-oriented conditioning during inference, enhancing action generation without adding explicit planning. The approach improves performance on RoboTwin2.0, LIBERO-40, and DexArt benchmarks, achieving higher success rates across multiple tasks.
arXiv AI
Sep 1
Motus2: A Self-Evolving General World Model for Dexterous Manipulation
Motus2 is a self‑evolving general world model designed for dexterous manipulation. It integrates a shared‑weight model that offers three control interfaces—a policy, a simulator, and an evaluator—forming a closed decision‑and‑learning loop for policy improvement. The system scales both model size and data, progressing from large‑scale monocular egocentric data to synchronized stereo data and robot‑domain adaptation, while also incorporating tactile feedback and a biomimetic platform with dual arms and hands.
Hugging Face Trending Papers
Jun 23
InSight: Self-Guided Skill Acquisition via Steerable VLAs
Vision-language-action (VLA) models can learn manipulation skills from demonstrations, but their capabilities are bounded by the skills in the training data. We present InSight, a framework that unlocks autonomous skill acquisition by rendering VLAs steerable at the primitive-action level (e.