Amphibious legged robots inspired by salamanders are promising in applications in complex amphibious environments. However, despite the significant success of training controllers that achieve diverse locomotion behaviors in conventional quadrupedal robots, most salamander robots relied on central-pattern-generator (CPG)-based and model-based coordination strategies for locomotion control. Learning unified joint-level whole-body control that reliably transfers from simulation to highly articulated physical salamander robots remains relatively underexplored. In addition, few legged robots have tried learning-based controllers in amphibious environments. In this work, we employ Reinforcement Learning to map proprioceptive observations and commanded velocities to joint-level actions, allowing coordinated locomotor behaviors to emerge. To deploy these policies on hardware, we adopt a system-level real-to-sim matching and sim-to-real transfer strategy. The learned controller achieves stable and coordinated walking on both flat and uneven terrains in the real world. Beyond terrestrial locomotion, the framework enables transitions between walking and swimming in simulation, highlighting a phenomenon of interest for understanding locomotion across distinct physical modes.

翻译：受蝾螈启发的两栖腿式机器人在复杂两栖环境中具有广阔的应用前景。然而，尽管在传统四足机器人中训练控制器以实现多样化运动行为已取得显著成功，但大多数蝾螈机器人仍依赖基于中枢模式发生器（CPG）和基于模型的协调策略进行运动控制。学习统一的关节级全身控制，并可靠地从仿真迁移到高度关节化的实体蝾螈机器人，仍是相对未被充分探索的领域。此外，鲜有腿式机器人在两栖环境中尝试基于学习的控制器。在本工作中，我们采用强化学习将本体感知观测与指令速度映射为关节级动作，从而涌现出协调的运动行为。为在硬件上部署这些策略，我们采用了系统级的实-仿匹配与仿-实迁移策略。学习得到的控制器在现实世界中的平坦与不平坦地形上均实现了稳定协调的行走。除了陆地运动外，该框架还在仿真中实现了行走与游泳之间的模式转换，凸显了理解跨不同物理模式运动机制的重要现象。