Amirhossein Hosseinmemar.
Push Recovery and Active Balancing for Inexpensive Humanoid Robots.
PhD thesis,
Department of Computer Science, University of Manitoba,
Winnipeg, Canada,
August 2019.
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 teensized 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. |
@phdthesis{HosseinmemarThesis,
author = {Amirhossein Hosseinmemar},
title = {Push Recovery and Active Balancing for Inexpensive Humanoid Robots},
school = {Department of Computer Science, University of Manitoba},
year = {2019},
address = {Winnipeg, Canada},
month = {August},
pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/HosseinmemarPhD.pdf},
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 teensized 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.}
}
Seth Fiawoo.
Independent Activity and Local Opportunity for Dynamic Robot Team Management in Dangerous Domains.
Master's thesis,
Department of Computer Science, University of Manitoba,
Winnipeg, Canada,
July 2019.
Abstract: |
Dangerous domains are a challenge for teams of heterogeneous robots, since robot losses may involve the loss of particular skills that might be rare in the domain. Previous research has resulted in a framework that allows teams to rebalance and recruit from the environment. However, there is currently no consideration of situations where agents may at times provide more useful work globally by not joining a team, or situations where it might be discovered that types of work might be associated with a given locality. My thesis extends this framework to give agents the ability to refuse to join teams and work for times on their own, by considering current satisfaction in the use of their skills, the likely rarity of their skills, and the distribution of places those skills are used in the environment. I examine this work in a simulated Urban Search and Rescue domain. My results show that in scenarios where a robot's special skills are rare and tasks requiring those skills are only available at a few xed locations in the environment, a robot is more useful if it suspends its team commitment to make itself available to all teams. |
@mastersthesis{fiawooThesis,
author = {Seth Fiawoo},
title = {Independent Activity and Local Opportunity for Dynamic Robot Team Management in Dangerous Domains},
school = {Department of Computer Science, University of Manitoba},
year = {2019},
address = {Winnipeg, Canada},
month = {July},
pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/FiawooMSc.pdf},
abstract = {Dangerous domains are a challenge for teams of heterogeneous robots, since robot losses may involve the loss of particular skills that might be rare in the domain. Previous research has resulted in a framework that allows teams to rebalance and recruit from the environment. However, there is currently no consideration of situations where agents may at times provide more useful work globally by not joining a team, or situations where it might be discovered that types of work might be associated with a given locality. My thesis extends this framework to give agents the ability to refuse to join teams and work for times on their own, by considering current satisfaction in the use of their skills, the likely rarity of their skills, and the distribution of places those skills are used in the environment. I examine this work in a simulated Urban Search and Rescue domain. My results show that in scenarios where a robot's special skills are rare and tasks requiring those skills are only available at a few xed locations in the environment, a robot is more useful if it suspends its team commitment to make itself available to all teams.}
}