  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.
