Learning CPG Sensory Feedback with Policy Gradient for Biped Locomotion for a Full-Body Humanoid

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Sim-to-Real: Learning Agile Locomotion For Quadruped Robots (2018, background): “Much research has applied reinforcement learning to locomotion tasks [2, 47, 11, 22, 31].”
Evaluation of Policy Gradient Methods and Variants on the Cart-Pole Benchmark (2007, background): “, anthromorphic robot arms [4], [8] or legged robots [2], [6], [13], require highly task specific adaptation of both the policy and the algorithm.”
Policy Gradient Methods for Robotics (2006, unknown): “Finally, this approach has yielded the most real-world robotics results [1], [2], [5], [7], [10], [12], [13].”
Reinforcement learning of motor skills with policy gradients (2008, background): “Finally, this approach has yielded the most realworld robot motor learning results (Benbrahim & Franklin, 1997; Endo et al., 2005; Gullapalli et al., 1994; Kimura & Kobayashi, 1997; Mori et al., 2004; Nakamura et al., 2004; Peters et al., 2005a).”
Learning Agile Robotic Locomotion Skills by Imitating Animals (2020, background): “Reinforcement learning has been effective for automatically acquiring locomotion skills in simulation [44, 34, 32] and in the real world [31, 59, 14, 58, 22, 26].”