Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Subject "Collision Avoidance"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Robotic Embodiment of Human-Like Motor Skills via Sim-to-Real Reinforcement Learning
    (2021-12) Guzman, Luis
    State of the art methods continue to face difficulties automating many tasks, particularly those which require human-like dexterity. The proposed "Internet of Skills" enables robots to learn advanced skills from a small set of expert demonstrations, bridging the gap between human and robot abilities. In this work, I train Reinforcement Learning (RL) control policies for the tasks of hand following and block pushing. I build a sim-to-real pipeline and demonstrate these policies on a Kinova Gen3 robot. Lastly, I test a prototype system that allows an expert to control the Kinova robot using only their arm movements, captured using a Vicon motion tracking system. My results show that performance of state of the art RL methods could be improved through the use of demonstrations, and I build a shared representation of human and robot action that will enable robots to learn new skills from observing expert actions.