We present a novel modularized end-to-end framework for legged reactive navigation in complex dynamic environments using a single light detection and ranging (LiDAR) sensor. The system comprises four simulation-trained modules: three reinforcement-learning (RL) policies for locomotion, safety shielding, and navigation, and a transformer-based exteroceptive estimator that processes raw point-cloud inputs. This modular decomposition of complex legged motor-control tasks enables lightweight neural networks with simple architectures, trained using standard RL practices with targeted reward shaping and curriculum design, without reliance on heuristics or sophisticated policy-switching mechanisms. We conduct comprehensive ablations to validate our design choices and demonstrate improved robustness compared to existing approaches in challenging navigation tasks. The resulting reactive safe navigation (REASAN) system achieves fully onboard and real-time reactive navigation across both single- and multi-robot settings in complex environments. We release our training and deployment code at https://github.com/ASIG-X/REASAN.

翻译：我们提出了一种新颖的模块化端到端框架，用于腿式机器人在复杂动态环境中的反应式导航，仅使用单个激光雷达传感器。该系统包含四个在仿真中训练的模块：三个用于运动、安全防护和导航的强化学习策略，以及一个基于Transformer的外感知估计器，用于处理原始点云输入。这种对复杂腿式运动控制任务的模块化解耦，使得我们能够采用结构简单的轻量级神经网络，通过标准强化学习实践、有针对性的奖励塑造和课程设计进行训练，无需依赖启发式方法或复杂的策略切换机制。我们进行了全面的消融实验以验证设计选择，并在具有挑战性的导航任务中展示了相较于现有方法更强的鲁棒性。最终的反应式安全导航系统能够在复杂环境中实现完全机载、实时的反应式导航，适用于单机器人和多机器人场景。我们在https://github.com/ASIG-X/REASAN发布了训练和部署代码。