aiqbench analysis
SWIM addresses a critical bottleneck in physically-based motion generation by introducing a compact, low-dimensional representation for dense fluid environments, enabling deep reinforcement learning to scale where traditional rigid-fluid simulations are too slow. By tokenizing per-link body-water forces and torques, this framework achieves superior stability and generalization across varying fluid densities and swimming styles without relying on imitation learning. This advancement represents a significant step forward in applying advanced machine learning techniques to complex, continuous physical interactions that were previously unsolved for human-scale agents.
SWIM: Compact Environment Representation for Reinforcement Learning Human Swimming
arXiv:2605.31120v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (RL) has driven rapid progress in physically-based motion generation, yet synthesizing robust motion policies in dense fluid environments (e.g. swimming) remains unsolved. Unlike land motion, where environmental impact is sparse and can be coarsely modeled (e.g. gravity, normal reaction), swimming requires continuous, full-body coordination under pressure and flow forces across the entire body surface; fully-coupled rigid-fluid simulation is far too slow for the millions of interactions RL requires. We propose SWIM, an RL framework for physically-based human swimming learned from a single reference motion. Its core is a compact, low-dimensional environment representation: a dual-branch graph-convolutional VQ-VAE that tokenizes per-link body-water forces and torques, informative enough for control yet robust to the rapidly changing force exchanges that would otherwise destabilize training. We pair this with a GPU-based Lagrangian solver with rigid-fluid coupling that yields 10-15x faster training. Across hundreds of training runs and several hundred zero-shot conditions, SWIM generalizes to unseen goals and trajectories, fluids with varying density, external flows and perturbations, altered body morphologies, and all four competitive swimming styles. Against imitation-learning baselines (MimicKit-DeepMimic, MimicKit-AMP, ADD) and alternative force models (a PINN world model, MuJoCo's inertial fluid model, and an underwater-robotics simulator), SWIM achieves better stability, goal satisfaction, and physical realism: a 63.9% success rate on held-out generalization versus 36.8% for the strongest baseline.
Abstract:Deep reinforcement learning (RL) has driven rapid progress in physically-based motion generation, yet synthesizing robust motion policies in dense fluid environments (e.g. swimming) remains unsolved. Unlike land motion, where environmental impact is sparse and can be coarsely modeled (e.g. gravity, normal reaction), swimming requires continuous, full-body coordination under pressure and flow forces across the entire body surface; fully-coupled rigid-fluid simulation is far too slow for the millions of interactions RL requires. We propose SWIM, an RL framework for physically-based human swimming learned from a single reference motion. Its core is a compact, low-dimensional environment representation: a dual-branch graph-convolutional VQ-VAE that tokenizes per-link body-water forces and torques, informative enough for control yet robust to the rapidly changing force exchanges that would otherwise destabilize training. We pair this with a GPU-based Lagrangian solver with rigid-fluid coupling that yields 10-15x faster training. Across hundreds of training runs and several hundred zero-shot conditions, SWIM generalizes to unseen goals and trajectories, fluids with varying density, external flows and perturbations, altered body morphologies, and all four competitive swimming styles. Against imitation-learning baselines (MimicKit-DeepMimic, MimicKit-AMP, ADD) and alternative force models (a PINN world model, MuJoCo's inertial fluid model, and an underwater-robotics simulator), SWIM achieves better stability, goal satisfaction, and physical realism: a 63.9% success rate on held-out generalization versus 36.8% for the strongest baseline.
20 pages. Project page: this https URL
Graphics (cs.GR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2605.31120 [cs.GR]
(or arXiv:2605.31120v2 [cs.GR] for this version)
https://doi.org/10.48550/arXiv.2605.31120 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 321 words
Related coverage
[AINews] The Future of Latent Space
Latent Space · Sep 25, 2026
[AINews] Opus 5.5 is good at explainer videos
Latent Space · Sep 29, 2026
World Models and Yann LeCun’s Bet Against Large Language Models
Quantum Zeitgeist · Jul 17, 2026
Latent reasoning architectures would likely undermine CoT, our strongest oversight tool
AI Alignment Forum · Sep 23, 2026