SWIM: Compact Environment Representation for Reinforcement Learning Human Swimming

Excerpt

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-co