Kavli Affiliate: Ke Wang
| First 5 Authors: Rui Zong, Martin Liang, Yuntian Fang, Ke Wang, Xiaoshuai Chen
| Summary:
Knee-less bipedal robots like SLIDER have the advantage of ultra-lightweight
legs and improved walking energy efficiency compared to traditional humanoid
robots. In this paper, we firstly introduce an improved hardware design of the
bipedal robot SLIDER with new line-feet and more optimized mass distribution
which enables higher locomotion speeds. Secondly, we propose an extended Hybrid
Zero Dynamics (eHZD) method, which can be applied to prismatic joint robots
like SLIDER. The eHZD method is then used to generate a library of gaits with
varying reference velocities in an offline way. Thirdly, a Guided Deep
Reinforcement Learning (DRL) algorithm is proposed to use the pre-generated
library to create walking control policies in real-time. This approach allows
us to combine the advantages of both HZD (for generating stable gaits with a
full-dynamics model) and DRL (for real-time adaptive gait generation). The
experimental results show that this approach achieves 150% higher walking
velocity than the previous MPC-based approach.
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