深層強化学習による全方位二足歩行
DeepWalk: Omnidirectional Bipedal Gait by Deep Reinforcement Learning
単一のニューラルネットワークで二足歩行ロボットの全方位移動を学習させるDRL手法を提案し、目標速度を段階的に難しくするカリキュラム学習とsim-to-real転移により実機で動作させた。
著者: Diego Rodriguez, Sven Behnke
分類: cs.RO
原文アブストラクト
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.
