![]() In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. Xie, Z., Berseth, G., Clary, P., Hurst, J., van de Panne, M.: Feedback control for cassie with deep reinforcement learning. Hwangbo, J., et al.: Learning agile and dynamic motor skills for legged robots. Heess, N., et al.: Emergence of locomotion behaviours in rich environments. arXiv preprint arXiv:1707.02286 (2017) FERL may also be extended to other legged robots to generate various locomotion styles, which provides a competitive alternative for imitation learning. Comparison results show that the feedforward is key to generating humanlike behavior, while the policy trained with no feedforward only results in some strange gaits. By using FERL with a simple feedforward of two feet stepping up and down alternately, we achieve humanlike walking and running for a simulated biped robot, Ranger Max. In FERL, the control action is composed of a feedforward part and a feedback part, where the feedforward part is a periodic time-dependent signal generated by a state machine and the feedback part is a state-dependent signal obtained by a neural network. Here we propose a novel and simple way to generate humanlike behavior by using feedforward enhanced reinforcement learning (FERL). ![]() Although imitation learning provides a way to mimic the behavior of humans or animals, the obtained motion may be restricted due to the over-constrained property of this method. However, the reward signal design remains a challenging problem to produce a humanlike motion such as walking and running. Recently, reinforcement learning has been applied to legged locomotion and made a great success. Locomotion control of legged robots is a challenging problem.
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