DeepWalk: Omnidirectional Bipedal Gait by Deep Reinforcement Learning
This addresses the problem of complex, adaptable locomotion for robotics, offering a method that is robot-agnostic and reduces design complexity, though it is incremental in applying DRL to bipedal gait.
The paper tackled the challenge of enabling omnidirectional bipedal walking for humanoid robots using deep reinforcement learning, achieving this with a single control policy and successfully transferring it to a real robot without needing reference motions.
Bipedal walking is one of the most difficult but exciting challenges in robotics. The difficulties arise from the complexity of high-dimensional dynamics, sensing and actuation limitations combined with real-time and computational constraints. Deep Reinforcement Learning (DRL) holds the promise to address these issues by fully exploiting the robot dynamics with minimal craftsmanship. In this paper, we propose a novel DRL approach that enables an agent to learn omnidirectional locomotion for humanoid (bipedal) robots. Notably, the locomotion behaviors are accomplished by a single control policy (a single neural network). We achieve this by introducing a new curriculum learning method that gradually increases the task difficulty by scheduling target velocities. In addition, our method does not require reference motions which facilities its application to robots with different kinematics, and reduces the overall complexity. Finally, different strategies for sim-to-real transfer are presented which allow us to transfer the learned policy to a real humanoid robot.