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 multi-task reinforcement learning framework to
train the 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 its
short-term I/O history. In order to train a versatile multi-task policy, we
utilize a multi-stage training scheme that includes different training stages
for different objectives. After multi-stage training, the multi-task policy can
be directly transferred to Cassie, a physical bipedal robot. Training on
different tasks and exploring more diverse scenarios leads to highly robust
policies that can exploit the diverse set of learned skills to recover from
perturbations or poor landings during real-world deployment. Such robustness in
the proposed multi-task 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-axis jumps.

本文介绍了一种多任务强化学习框架，以训练扭矩控制双足机器人在现实世界中执行各种跳跃任务，通过不同的训练阶段和多样化的情境探索，最终实现了高鲁棒性的多任务策略，并为 Cassie 双足机器人在现实世界中完成各种具有挑战性的跳跃任务提供了支持。