为蝾螈机器人学习全身控制
Learning Whole-Body Control for a Salamander Robot
AI总结：
本文提出通过强化学习实现蝾螈机器人全身协调控制，解决从仿真到真实环境的迁移问题，并实现陆地与水下运动转换。
摘要：
受蝾螈启发的水生四足机器人在复杂水生环境中具有应用潜力。然而，大多数蝾螈机器人仍依赖中央模式发生器（CPG）和模型驱动的协调策略进行运动控制。学习统一的关节级全身控制，实现从仿真到高灵活物理蝾螈机器人的可靠迁移仍较为不足。此外，很少有腿式机器人尝试在水生环境中使用基于学习的控制器。本文采用强化学习将本体感觉观测和命令速度映射到关节级动作，使协调运动行为得以产生。为在硬件上部署这些策略，我们采用系统级真实到仿真匹配和仿真到真实迁移策略。所学控制器在现实世界中实现了在平坦和不平地形上的稳定协调行走。除了陆地运动外，该框架在仿真中实现了行走与游泳的转换，突显了理解不同物理模式下运动现象的兴趣。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
