  This work aims to push the limits of agility for bipedal robots by enabling a
torque-controlled bipedal robot to perform robust and versatile dynamic jumps
in the real world. We present a reinforcement learning framework for training a
robot to accomplish a large variety of jumping tasks, such as jumping to
different locations and directions. To improve performance on these challenging
tasks, we develop a new policy structure that encodes the robot's long-term
input/output (I/O) history while also providing direct access to a short-term
I/O history. In order to train a versatile jumping policy, we utilize a
multi-stage training scheme that includes different training stages for
different objectives. After multi-stage training, the policy can be directly
transferred to a real bipedal Cassie robot. Training on different tasks and
exploring more diverse scenarios lead to highly robust policies that can
exploit the diverse set of learned maneuvers to recover from perturbations or
poor landings during real-world deployment. Such robustness in the proposed
policy enables Cassie to succeed in completing a variety of challenging jump
tasks in the real world, such as standing long jumps, jumping onto elevated
platforms, and multi-axes jumps.
