标题： QuadKAN：通过端到端强化学习增强KAN的四足运动控制 显示英文标题

标题： QuadKAN: KAN-Enhanced Quadruped Motion Control via End-to-End Reinforcement Learning

摘要： 我们通过强化学习(RL)解决视觉引导的四足运动控制问题，并强调将本体感觉与视觉结合对于鲁棒控制的必要性。 我们提出了QuadKAN，一种用Kolmogorov-Arnold网络(KANs)实例化的样条参数化跨模态策略。 该框架包含一个用于本体感觉的样条编码器和一个用于本体感觉-视觉输入的样条融合头。 这种结构化的函数类将状态到动作的映射与步态的分段光滑特性对齐，提高了样本效率，减少了动作抖动和能耗，并提供了可解释的姿态-动作敏感性。 我们采用多模态延迟随机化(MMDR)，并通过近端策略优化(PPO)进行端到端训练。 在包括平坦和不平坦表面以及静态或动态障碍物场景在内的多种地形上的评估表明，QuadKAN在回报、距离和碰撞次数方面均优于最先进的(SOTA)基线方法。 这些结果表明，样条参数化策略为鲁棒的视觉引导运动提供了一种简单、有效且可解释的替代方案。 在被接受后将提供一个代码库。 显示英文摘要

摘要： We address vision-guided quadruped motion control with reinforcement learning (RL) and highlight the necessity of combining proprioception with vision for robust control. We propose QuadKAN, a spline-parameterized cross-modal policy instantiated with Kolmogorov-Arnold Networks (KANs). The framework incorporates a spline encoder for proprioception and a spline fusion head for proprioception-vision inputs. This structured function class aligns the state-to-action mapping with the piecewise-smooth nature of gait, improving sample efficiency, reducing action jitter and energy consumption, and providing interpretable posture-action sensitivities. We adopt Multi-Modal Delay Randomization (MMDR) and perform end-to-end training with Proximal Policy Optimization (PPO). Evaluations across diverse terrains, including both even and uneven surfaces and scenarios with static or dynamic obstacles, demonstrate that QuadKAN achieves consistently higher returns, greater distances, and fewer collisions than state-of-the-art (SOTA) baselines. These results show that spline-parameterized policies offer a simple, effective, and interpretable alternative for robust vision-guided locomotion. A repository will be made available upon acceptance.