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Biomimetics 2019, 4(1), 28; https://doi.org/10.3390/biomimetics4010028

Implementation of Deep Deterministic Policy Gradients for Controlling Dynamic Bipedal Walking

1
Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
2
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
*
Author to whom correspondence should be addressed.
Received: 12 November 2018 / Revised: 21 February 2019 / Accepted: 11 March 2019 / Published: 22 March 2019
(This article belongs to the Special Issue Selected Papers from Living Machines 2018)
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Abstract

A control system for bipedal walking in the sagittal plane was developed in simulation. The biped model was built based on anthropometric data for a 1.8 m tall male of average build. At the core of the controller is a deep deterministic policy gradient (DDPG) neural network that was trained in GAZEBO, a physics simulator, to predict the ideal foot placement to maintain stable walking despite external disturbances. The complexity of the DDPG network was decreased through carefully selected state variables and a distributed control system. Additional controllers for the hip joints during their stance phases and the ankle joint during toe-off phase help to stabilize the biped during walking. The simulated biped can walk at a steady pace of approximately 1 m/s, and during locomotion it can maintain stability with a 30 kg·m/s impulse applied forward on the torso or a 40 kg·m/s impulse applied rearward. It also maintains stable walking with a 10 kg backpack or a 25 kg front pack. The controller was trained on a 1.8 m tall model, but also stabilizes models 1.4–2.3 m tall with no changes. View Full-Text
Keywords: biped; DDPG neural network; gait; stability biped; DDPG neural network; gait; stability
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Liu, C.; Lonsberry, A.G.; Nandor, M.J.; Audu, M.L.; Lonsberry, A.J.; Quinn, R.D. Implementation of Deep Deterministic Policy Gradients for Controlling Dynamic Bipedal Walking. Biomimetics 2019, 4, 28.

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