SWIM: Compact Environment Representation for Reinforcement Learning Human Swimming
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 repr...
| Authors | Binglun Wang, Doğa Yılmaz, Niloy Mitra et al. |
|---|---|
| Primary category | cs.GR |
| Published | 29 May 2026 |
| Last updated at source | 2 October 2026 |
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