Push recovery and active balancing for inexpensive humanoid robots
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Date
2019-08-28
Authors
Hosseinmemar, Amirhossein
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Abstract
Active balancing of a humanoid robot is a challenging task due to the complexity
of combining a walking gait, dynamic balancing, vision and high-level behaviors.
My Ph.D research focuses on the active balancing and push recovery problems that
allow inexpensive humanoid robots to balance while standing and walking, and to
compensate for external forces. In this research, I have proposed a push recovery
mechanism that employs two machine learning techniques, Reinforcement Learning
(RL) and Deep Reinforcement Learning (DRL) to learn recovery step trajectories
during push recovery using a closed-loop feedback control. I have implemented a 3D
model using the Robot Operating System (ROS) and Gazebo. To reduce wear and
tear on the real robot, I used this model for learning the recovery steps for different
impact strengths and directions. I evaluated my approach in both in the real world
and in simulation. All the real world experiments are performed by Polaris, a teen-
sized humanoid robot in the Autonomous Agent Laboratory (AALab), University of
Manitoba. The design, implementation, and evaluation of hardware, software, and
kinematic models are discussed in this document.
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Keywords
Humanoid robot, Push recovery, Active balancing, Deep reinforcement learning