This paper presents a real-time gait driven training framework for humanoid robots. First, we introduce a novel gait planner that incorporates dynamics to design the desired joint trajectory. In the gait design process, the 3D robot model is decoupled into two 2D models, which are then approximated as hybrid inverted pendulums (H-LIP) for trajectory planning. The gait planner operates in parallel in real time within the robot's learning environment. Second, based on this gait planner, we design three effective reward functions within a reinforcement learning framework, forming a reward composition to achieve periodic bipedal gait. This reward composition reduces the robot's learning time and enhances locomotion performance. Finally, a gait design example, along with simulation and experimental comparisons, is presented to demonstrate the effectiveness of the proposed method.

翻译：本文提出了一种用于仿人机器人的实时步态驱动训练框架。首先，我们引入了一种新颖的步态规划器，该规划器结合动力学来设计期望的关节轨迹。在步态设计过程中，三维机器人模型被解耦为两个二维模型，随后将其近似为混合倒立摆（H-LIP）进行轨迹规划。该步态规划器在机器人的学习环境中实时并行运行。其次，基于此步态规划器，我们在强化学习框架内设计了三种有效的奖励函数，构成一种奖励组合以实现周期性的双足步态。该奖励组合减少了机器人的学习时间并提升了运动性能。最后，通过一个步态设计实例以及仿真与实验对比，展示了所提方法的有效性。