Learning dynamic whole-body motions for legged robots through reinforcement learning (RL) remains challenging due to the high risk of failure, which makes efficient exploration difficult and often leads to unstable learning. In this paper, we propose External Force Guided Curriculum Learning (EFGCL), a guided RL approach based on the principle of physical guidance, in which external assistive forces are introduced during training. Inspired by spotting in artistic gymnastics, EFGCL enables agents to physically experience successful motion executions without relying on task-specific reward shaping or reference trajectories. Experiments on a quadrupedal robot performing Jump, Backflip, and Lateral-Flip tasks demonstrate that EFGCL accelerates learning of the Jump task by approximately a factor of two and enables the acquisition of complex whole body motions that conventional RL methods fail to learn. We further show that the learned policies can be deployed on real robot, reproducing motions consistent with those observed in simulation. These results indicate that physically guided exploration, which allows agents to experience success early in training, is an effective and general strategy for improving learning efficiency in dynamic whole-body motion tasks.

翻译：通过强化学习（RL）让足式机器人学习动态全身运动仍具有挑战性，因为高失败风险导致高效探索困难，并常引发不稳定的学习过程。本文提出外力引导课程学习（EFGCL），一种基于物理引导原理的引导式RL方法，在训练过程中引入外部辅助力。受艺术体操中保护动作的启发，EFGCL使智能体无需依赖特定任务奖励塑造或参考轨迹，就能物理体验成功运动执行。在四足机器人执行的跳跃、后空翻和侧空翻任务实验中，EFGCL将跳跃任务的学习速度提升约两倍，并使得常规RL方法无法学习的复杂全身运动成为可能。我们进一步证明，学习到的策略可部署至真实机器人，复现与仿真中观察到的一致运动。这些结果表明，允许智能体在训练早期体验成功的物理引导探索，是提升动态全身运动任务学习效率的有效通用策略。