Abstract
This paper presents a framework to generate energy efficient dynamic human-like walk for a Nao humanoid robot. We first extend the inverted pendulum model with the goal of finding an energy efficient and stable walking gait. In this model, we propose a leg control policy which utilizes joint stiffness control. We use policy gradient reinforcement learning to identify the optimal parameters of the new gait for a Nao humanoid robot. We successfully test the control policy in a simulator and on a real Nao robot. The test results show that the new control policy realizes a dynamic walk that is more energy efficient than the standard walk of Nao robot.
Original language | English |
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Title of host publication | 2014 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2014 |
Publisher | IEEE Computer Society |
Pages | 267-272 |
Number of pages | 6 |
ISBN (Print) | 978-1-4799-4254-1 |
DOIs | |
Publication status | Published - 2014 |
Keywords
- Energy-efficient
- Humanoid Robot
- Learning Control