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Vinsamlegast notið þetta auðkenni þegar þið vitnið til verksins eða tengið í það: http://hdl.handle.net/1946/34968

Titill: 
  • Titill er á ensku Qbot: Quadrupedal Ambulation via Reinforcement Learning
Námsstig: 
  • Meistara
Útdráttur: 
  • Útdráttur er á ensku

    Ambulation is a valuable form of locomotion for robots which must operate in spaces designed for human foot traffic or over uneven terrain. However, traditional approaches to robotic ambulation are laborious to implement. Recent advances in deep reinforcement learning have made it a promising alternative, but previous attempts have relied on detailed physics engine modeling for training in simulation. This project has developed a program which can teach a quadrupedal robot how to walk in real time regardless of its dimensions or configuration. The strategy presented in this project can be applied to larger, more robust quadrupeds which might serve some practical purpose. Additionally, a new reinforcement learning algorithm was discovered in the course of this research which may find applications across a wide variety of reinforcement learning problems.

Tengd vefslóð: 
Samþykkt: 
  • 5.2.2020
URI: 
  • http://hdl.handle.net/1946/34968


Skrár
Skráarnafn Stærð AðgangurLýsingSkráartegund 
Qbot_Final_Report_v6.pdf10.76 MBOpinnHeildartextiPDFSkoða/Opna
declaration.pdf2.48 MBLokaðurYfirlýsingPDF