A Study of the Efficacy of Generative Flow Networks for Robotics and Machine Fault-Adaptation

• Author / Creator

  Sufiyan, Zahin

• In 2005, Opportunity, one of NASA’s renowned Mars rovers, faced a dire situation.
  It was a moment that could end a mission that had already far outlasted its expected
  lifespan. After clambering out of the Victoria crater, the rover started to experience
  an abrupt current spike in its right front wheel. As a consequence, the wheel motor
  started to malfunction, causing the wheel to stop turning. NASA anticipated that
  the $400 million dollars of investment and most importantly invaluable scientific data
  regarding the Martian terrain was on the brink of remaining unexplored. With the
  immensity of space between Mars and Earth (140 million miles, to be specific), the
  engineers at NASA could only detect and diagnose the malfunction; however, human
  intervention in the maintenance of Opportunity was an impossibility. Nevertheless,
  human ingenuity again succeeded when the engineers at NASA’s Jet Propulsion Laboratory
  came up with an unconventional workaround. They started to drive the rover
  backward and thus by doing so, they were able to redistribute the mechanical load
  and reduce the strain on the malfunctioning wheel. The impaired wheel now functioned
  as a rear wheel, allowing the fully functional wheels to lead and navigate the
  harsh and challenging surface of Mars. Due to this innovative approach, Opportunity
  continued to explore Mars and gathered some of the most invaluable data about the
  red planet for 15 Earth years instead of its initially predicted 90-day lifespan. This
  was a testament to human ingenuity, but also a stark reminder of the necessity for
  built-in machine fault adaptability in robotic systems.
  Our research is a step towards adding hardware fault tolerance and fault adaptability
  to machines. In this research, our primary focus is to investigate the efficacy of generative flow networks (GFlowNets/CFlowNets) in robotic environments, particularly
  in the domain of machine fault adaptation. Generative Flow Networks is an
  emerging algorithm with the potential to be considered as a substitute approach to
  the prevalent reinforcement learning methods in continuous exploratory tasks. In our
  work, the experimentations were done in a simulated robotic environment (Reacherv2).
  This environment was manipulated and modified to introduce four distinct
  fault environments which are reduced range of motion, increased damping, actuator
  damage, and structural damage. Each fault replicates actual malfunctions that
  are generally witnessed in real-world machines/robots that render them inoperative.
  The empirical evaluation of this research indicates that continuous generative flow
  networks indeed have the capability to add adaptive behaviors in machines under
  adversarial conditions in the environment. Furthermore, the comparative analysis of
  CFlowNets with state-of-the-art RL algorithms also provides some key insights into
  the performance in terms of adaptation speed and sample efficiency. Despite a few
  algorithmic shortcomings, our experiments confirm that CFlowNets has the potential
  to be deployed in a real-world machine and it can demonstrate adaptability in case of
  malfunctions to maintain functionality. The thesis is motivated by the idea of transforming
  robots into more than just mere tools, making them capable entities which
  are capable of autonomously overcoming certain faults and failures, thus sustaining
  their operation while delaying the need for maintenance. Through experimentation
  in simulated robotic environments, the comparative study aims to contribute to the
  ongoing discourse on enhancing the adaptive capacities of automated systems and
  machines.

• Subjects / Keywords

  • GFlowNets
  • Fault-Adaptation
  • CFlowNets

• Graduation date

  Spring 2024

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-nkjp-6c73

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.