Sim2Swim: Zero-Shot Velocity Control for Agile AUV Maneuvering in 3 Minutes

• URL: http://arxiv.org/abs/2512.08656v1
• Date: Tue, 09 Dec 2025 14:42:26 GMT
• Title: Sim2Swim: Zero-Shot Velocity Control for Agile AUV Maneuvering in 3 Minutes
• Abstract summary: Holonomic autonomous underwater vehicles (AUVs) have the hardware ability for agile maneuvering in both translational and rotational degrees of freedom (DOFs)<n>This paper presents the first general zero-shot sim2real deep reinforcement learning-based (DRL) velocity controller enabling path following and agile 6DOF maneuvering with a training duration of just 3 minutes.
• Score: 2.6511518230332753
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Holonomic autonomous underwater vehicles (AUVs) have the hardware ability for agile maneuvering in both translational and rotational degrees of freedom (DOFs). However, due to challenges inherent to underwater vehicles, such as complex hydrostatics and hydrodynamics, parametric uncertainties, and frequent changes in dynamics due to payload changes, control is challenging. Performance typically relies on carefully tuned controllers targeting unique platform configurations, and a need for re-tuning for deployment under varying payloads and hydrodynamic conditions. As a consequence, agile maneuvering with simultaneous tracking of time-varying references in both translational and rotational DOFs is rarely utilized in practice. To the best of our knowledge, this paper presents the first general zero-shot sim2real deep reinforcement learning-based (DRL) velocity controller enabling path following and agile 6DOF maneuvering with a training duration of just 3 minutes. Sim2Swim, the proposed approach, inspired by state-of-the-art DRL-based position control, leverages domain randomization and massively parallelized training to converge to field-deployable control policies for AUVs of variable characteristics without post-processing or tuning. Sim2Swim is extensively validated in pool trials for a variety of configurations, showcasing robust control for highly agile motions.

Related papers

• CORAL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles [14.192376456398456]
  Fine grain control and positioning of autonomous underwater vehicles (AUVs) is critical for sampling, maintenance, and survey applications.<n>Traditional control methods for AUVs are labor intensive and are not robust to changes in the vehicle configuration or environmental conditions.<n>We are the first to successfully deploy a zero-shot RL policy on a 6-DOF AUV in which policy training is performed on surrogate drag models (SDMs) trained on CFD data.
  arXiv  Detail & Related papers  (2026-07-10T16:02:55Z)
• Sim-to-reality adaptation for Deep Reinforcement Learning applied to an underwater docking application [0.0]
  Deep Reinforcement Learning (DRL) offers a robust alternative to traditional control methods for autonomous underwater docking.<n>This paper presents a systematic approach for autonomous docking using the Girona Autonomous Underwater Vehicle (AUV) by leveraging a high-fidelity digital twin environment.
  arXiv  Detail & Related papers  (2026-03-12T15:01:25Z)
• Fast Policy Learning for 6-DOF Position Control of Underwater Vehicles [1.4883782513177095]
  Reinforcement learning (RL) provides a powerful alternative but training is typically slow and sim-to-real transfer remains challenging.<n>This work introduces a GPU-accelerated RL training pipeline built in JAX and MuJoCo-XLA (MJX)<n>By jointly JIT-compiling large-scale parallel physics simulation and learning updates, we achieve training times of under two minutes.
  arXiv  Detail & Related papers  (2025-12-15T14:12:32Z)
• Forecasting Continuous Non-Conservative Dynamical Systems in SO(3) [51.510040541600176]
  We propose a novel approach to modeling the rotation of moving objects in computer vision.<n>Our approach is agnostic to energy and momentum conservation while being robust to input noise.<n>By learning to approximate object dynamics from noisy states during training, our model attains robust extrapolation capabilities in simulation and various real-world settings.
  arXiv  Detail & Related papers  (2025-08-11T09:03:10Z)
• SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data Pretraining [62.433137130087445]
  SuperFlow++ is a novel framework that integrates pretraining and downstream tasks using consecutive camera pairs.<n>We show that SuperFlow++ outperforms state-of-the-art methods across diverse tasks and driving conditions.<n>With strong generalizability and computational efficiency, SuperFlow++ establishes a new benchmark for data-efficient LiDAR-based perception in autonomous driving.
  arXiv  Detail & Related papers  (2025-03-25T17:59:57Z)
• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [57.278726604424556]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers.<n>Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy.<n>We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• Learning to Fly in Seconds [7.259696592534715]
  We show how curriculum learning and a highly optimized simulator enhance sample complexity and lead to fast training times. Our framework enables Simulation-to-Reality (Sim2Real) transfer for direct control after only 18 seconds of training on a consumer-grade laptop.
  arXiv  Detail & Related papers  (2023-11-22T01:06:45Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning [3.1635451288803638]
  We present a DRL-based real-time feedback strategy to control the hydrodynamic force on fluidic pinball. By adequately designing reward functions and encoding historical observations, the DRL-based control was shown to make reasonable and valid control decisions. One of these results was analyzed by a machine learning model that enabled us to shed light on the basis of decision-making and physical mechanisms of the force tracking process.
  arXiv  Detail & Related papers  (2023-04-23T03:39:50Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.