标题： LocoMamba：通过Mamba的端到端深度强化学习实现视觉驱动的运动 显示英文标题

标题： LocoMamba: Vision-Driven Locomotion via End-to-End Deep Reinforcement Learning with Mamba

摘要： 我们引入了LocoMamba，这是一个基于选择性状态空间模型的视觉驱动跨模态深度强化学习框架，特别利用了Mamba，实现了接近线性时间的序列建模，有效捕捉长距离依赖关系，并实现了更长序列的高效训练。 首先，我们使用多层感知机嵌入本体感受状态，并使用轻量级卷积神经网络对深度图像进行分块处理，生成紧凑的标记，以改善状态表示。 其次，堆叠的Mamba层通过接近线性时间的选择性扫描融合这些标记，降低延迟和内存占用，保持对标记长度和图像分辨率的鲁棒性，并提供一种归纳偏差以减轻过拟合。 第三，我们在地形和外观随机化以及障碍物密度课程下，使用近端策略优化对策略进行端到端训练，采用一种紧凑的状态中心奖励，平衡进展、平滑性和安全性。 我们在具有静态和移动障碍物以及不平坦地形的具有挑战性的模拟环境中评估我们的方法。 与最先进的基线方法相比，我们的方法在较少碰撞的情况下实现了更高的回报和成功率，表现出对未见过的地形和障碍物密度更强的泛化能力，并在相同的计算预算下通过更少的更新次数收敛，从而提高了训练效率。 显示英文摘要

摘要： We introduce LocoMamba, a vision-driven cross-modal DRL framework built on selective state-space models, specifically leveraging Mamba, that achieves near-linear-time sequence modeling, effectively captures long-range dependencies, and enables efficient training with longer sequences. First, we embed proprioceptive states with a multilayer perceptron and patchify depth images with a lightweight convolutional neural network, producing compact tokens that improve state representation. Second, stacked Mamba layers fuse these tokens via near-linear-time selective scanning, reducing latency and memory footprint, remaining robust to token length and image resolution, and providing an inductive bias that mitigates overfitting. Third, we train the policy end-to-end with Proximal Policy Optimization under terrain and appearance randomization and an obstacle-density curriculum, using a compact state-centric reward that balances progress, smoothness, and safety. We evaluate our method in challenging simulated environments with static and moving obstacles as well as uneven terrain. Compared with state-of-the-art baselines, our method achieves higher returns and success rates with fewer collisions, exhibits stronger generalization to unseen terrains and obstacle densities, and improves training efficiency by converging in fewer updates under the same compute budget.