Deep reinforcement learning (DRL) has emerged as a promising solution to mastering explosive and versatile quadrupedal jumping skills. However, current DRL-based frameworks usually rely on pre-existing reference trajectories obtained by capturing animal motions or transferring experience from existing controllers. This work aims to prove that learning dynamic jumping is possible without relying on imitating a reference trajectory by leveraging a curriculum design. Starting from a vertical in-place jump, we generalize the learned policy to forward and diagonal jumps and, finally, we learn to jump across obstacles. Conditioned on the desired landing location, orientation, and obstacle dimensions, the proposed approach yields a wide range of omnidirectional jumping motions in real-world experiments. Particularly we achieve a 90cm forward jump, exceeding all previous records for similar robots reported in the existing literature. Additionally, the robot can reliably execute continuous jumping on soft grassy grounds, which is especially remarkable as such conditions were not included in the training stage. A supplementary video can be found on: https://www.youtube.com/watch?v=nRaMCrwU5X8. The code associated with this work can be found on: https://github.com/Vassil17/Curriculum-Quadruped-Jumping-DRL.

翻译：深度强化学习（DRL）已成为掌握爆发性和多功能四足跳跃技能的一种有前景的解决方案。然而，当前基于DRL的框架通常依赖预先存在的参考轨迹，这些轨迹通过捕捉动物运动或从现有控制器迁移经验获得。本研究旨在证明，通过利用课程设计，无需模仿参考轨迹即可实现动态跳跃学习。从原地垂直跳跃开始，我们将所学策略推广至前向和斜向跳跃，最终学习跨越障碍物。基于期望的着陆位置、朝向和障碍物尺寸，所提出的方法在实际实验中生成了广泛的全向跳跃运动。特别地，我们实现了90厘米的前向跳跃，超越了现有文献中类似机器人的所有先前记录。此外，机器人能够在柔软的草地上可靠地执行连续跳跃，这一点尤为显著，因为此类条件并未包含在训练阶段中。补充视频可参见：https://www.youtube.com/watch?v=nRaMCrwU5X8。与本工作相关的代码可参见：https://github.com/Vassil17/Curriculum-Quadruped-Jumping-DRL。