An energy efficient dynamic gait for a Nao robot

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.

[1]  Arthur D Kuo,et al.  The six determinants of gait and the inverted pendulum analogy: A dynamic walking perspective. , 2007, Human movement science.

[2]  Andy Ruina,et al.  A Bipedal Walking Robot with Efficient and Human-Like Gait , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[3]  Nico Roos,et al.  An energy efficient gait for a Nao robot , 2013 .

[4]  James S. Welsh,et al.  A low power walk for the NAO robot , 2008 .

[5]  Michael Günther,et al.  Joint stiffness of the ankle and the knee in running. , 2002, Journal of biomechanics.

[6]  Yannick Aoustin,et al.  Optimal walking gait with double support, simple support and impact for a bipedal robot equipped of four-bar knees , 2012 .

[7]  Shuuji Kajita,et al.  Study of dynamic biped locomotion on rugged terrain-derivation and application of the linear inverted pendulum mode , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[8]  J. Delisa.  Gait analysis in the science of rehabilitation , 1998 .

[9]  Mingguo Zhao,et al.  The instantaneous leg extension model of Virtual Slope Walking , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Manoj Srinivasan,et al.  Computer optimization of a minimal biped model discovers walking and running , 2006, Nature.

[11]  Mingguo Zhao,et al.  High-speed and energy-efficient biped locomotion based on Virtual Slope Walking , 2011, Auton. Robots.

[12]  Shuuji Kajita,et al.  Real-time 3D walking pattern generation for a biped robot with telescopic legs , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[13]  Stefan Schaal,et al.  Policy Gradient Methods for Robotics , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Jan Peters,et al.  Toward fast policy search for learning legged locomotion , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Peter Stone,et al.  Machine Learning for Fast Quadrupedal Locomotion , 2004, AAAI.

[16]  Ambarish Goswami,et al.  Postural Stability of Biped Robots and the Foot-Rotation Indicator (FRI) Point , 1999, Int. J. Robotics Res..

[17]  Minhyung Lee,et al.  Optimal gait primitives for dynamic bipedal locomotion , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.