用强化学习让四足机器人精准跳远跳高，实测水平跳1.25米、垂直跳1.0米。
Towards Quadrupedal Jumping and Walking for Dynamic Locomotion using Reinforcement Learning
- 通过弹道运动规律增强稀疏奖励，加速跳跃策略学习。
- 水平跳1.25米精度达厘米级，垂直跳1.0米，超越已有方法。
- 可扩展至全向跳跃，适合动态复杂地形的四足机器人研究。
本文提出一种基于课程学习的强化学习框架，用于训练四足机器人Olympus的精确高效跳跃策略。针对垂直与水平跳跃分别设计独立策略，采用基于抛物线运动规律的奖励稠密化方法，提升稀疏奖励信号有效性；引入参考状态初始化机制，在无需参考轨迹的情况下加速动态跳跃行为探索。同时构建行走策略，与跳跃策略协同实现多样化动态运动能力。大量测试验证了其在不同地形上的行走性能及跳跃表现，显著优于先前工作，有效缩小了仿真到现实的差距。实验表明，水平跳跃可达1.25米，定位精度达厘米级；垂直跳跃最高达1.0米。此外，仅需微小调整即可扩展为全向跳跃策略。
原文摘要 · Abstract (English)
This paper presents a curriculum-based reinforcement learning framework for training precise and high-performance jumping policies for the robot `Olympus'. Separate policies are developed for vertical and horizontal jumps, leveraging a simple yet effective strategy. First, we densify the inherently sparse jumping reward using the laws of projectile motion. Next, a reference state initialization scheme is employed to accelerate the exploration of dynamic jumping behaviors without reliance on reference trajectories. We also present a walking policy that, when combined with the jumping policies, unlocks versatile and dynamic locomotion capabilities. Comprehensive testing validates walking on varied terrain surfaces and jumping performance that exceeds previous works, effectively crossing the Sim2Real gap. Experimental validation demonstrates horizontal jumps up to 1.25 m with centimeter accuracy and vertical jumps up to 1.0 m. Additionally, we show that with only minor modifications, the proposed method can be used to learn omnidirectional jumping.
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