Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
CoRR(2024)
摘要
This paper presents a comprehensive study on using deep reinforcement
learning (RL) to create dynamic locomotion controllers for bipedal robots.
Going beyond focusing on a single locomotion skill, we develop a general
control solution that can be used for a range of dynamic bipedal skills, from
periodic walking and running to aperiodic jumping and standing. Our RL-based
controller incorporates a novel dual-history architecture, utilizing both a
long-term and short-term input/output (I/O) history of the robot. This control
architecture, when trained through the proposed end-to-end RL approach,
consistently outperforms other methods across a diverse range of skills in both
simulation and the real world.The study also delves into the adaptivity and
robustness introduced by the proposed RL system in developing locomotion
controllers. We demonstrate that the proposed architecture can adapt to both
time-invariant dynamics shifts and time-variant changes, such as contact
events, by effectively using the robot's I/O history. Additionally, we identify
task randomization as another key source of robustness, fostering better task
generalization and compliance to disturbances. The resulting control policies
can be successfully deployed on Cassie, a torque-controlled human-sized bipedal
robot. This work pushes the limits of agility for bipedal robots through
extensive real-world experiments. We demonstrate a diverse range of locomotion
skills, including: robust standing, versatile walking, fast running with a
demonstration of a 400-meter dash, and a diverse set of jumping skills, such as
standing long jumps and high jumps.
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