SmoothTurn: 为四足机器人敏捷导航学习平稳转向
SmoothTurn: Learning to Turn Smoothly for Agile Navigation with Quadrupedal Robots
AI总结：
本文提出SmoothTurn框架，通过引入序列目标到达奖励、扩展观测空间和自动目标课程，使四足机器人在高速运行时实现平稳转向，提升敏捷导航能力。
摘要：
四足机器人在消防救援和工业检测等应用中展现出巨大潜力，但现有方法在高速运行时难以实现平稳转向。本文将任务定义为序列局部导航，提出SmoothTurn框架，通过新颖的奖励机制、扩展观测空间和自动目标课程，使机器人在高速运行时实现平稳转向。训练的策略可直接部署于配备传感器和计算单元的四足机器人上。仿真和实测结果表明，SmoothTurn学习了能实现目标间平稳转向的敏捷运动策略，表现出控制动量、面向未来目标和规划高效路径等涌现行为。
英文摘要：
Quadrupedal robots show great potential for valuable real-world applications such as fire rescue and industrial inspection. Such applications often require urgency and the ability to navigate agilely, which in turn demands the capability to change directions smoothly while running in high speed. Existing approaches for agile navigation typically learn a single-goal reaching policy by encouraging the robot to stay at the target position after reaching there. As a result, when the policy is used to reach sequential goals that require changing directions, it cannot anticipate upcoming maneuvers or maintain momentum across the switch of goals, thereby preventing the robot from fully exploiting its agility potential. In this work, we formulate the task as sequential local navigation, extending the single-goal-conditioned local navigation formulation in prior work. We then introduce SmoothTurn, a learning-based control framework that learns to turn smoothly while running rapidly for agile sequential local navigation. The framework adopts a novel sequential goal-reaching reward, an expanded observation space with a lookahead window for future goals, and an automatic goal curriculum that progressively expands the difficulty of sampled goal sequences based on the goal-reaching performance. The trained policy can be directly deployed on real quadrupedal robots with onboard sensors and computation. Both simulation and real-world empirical results show that SmoothTurn learns an agile locomotion policy that performs smooth turning across goals, with emergent behaviors such as controlling momentum when switching goals, facing towards the future goal in advance, and planning efficient paths.
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。
