Uploaded May 2024 | Updated September 2026, 25 minutes ago
Microsoft Future Leaders in Robotics and AI Seminar Series: Choreorobotics: Teaching Robots How to Dance with Humans
Online Seminar
Catie Cuan
Postdoctoral Fellow
Standford University
As robots transition from industrial and research settings into everyday environments, robots must be able to (1) learn from humans while benefiting from the full range of the humans' knowledge and (2) learn to interact with humans in safe, intuitive, and social ways. I will present a series of compelling robot behaviors, where human perception and interaction are foregrounded in a variety of tasks. Supervised learning is incorporated in three projects to improve robots’ capabilities in dynamic, changing environments. In the first project, robots learned a door opening task from human teleoperators. In the second project, robots learned to navigate in response to human gestures by using imitation learning paired with model predictive control. In the third project, robots learned to move in groups based on a choreographer's preferences while generating music in real time. This work on compelling robot behaviors elucidates how teaching interfaces and interactions with robots in everyday settings can be appealing, efficient, and delightful.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
Microsoft Future Leaders in Robotics and AI Seminar Series: Choreorobotics: Teaching Robots How to Dance with Humans
Online Seminar
Catie Cuan
Postdoctoral Fellow
Standford University
As robots transition from industrial and research settings into everyday environments, robots must be able to (1) learn from humans while benefiting from the full range of the humans' knowledge and (2) learn to interact with humans in safe, intuitive, and social ways. I will present a series of compelling robot behaviors, where human perception and interaction are foregrounded in a variety of tasks. Supervised learning is incorporated in three projects to improve robots’ capabilities in dynamic, changing environments. In the first project, robots learned a door opening task from human teleoperators. In the second project, robots learned to navigate in response to human gestures by using imitation learning paired with model predictive control. In the third project, robots learned to move in groups based on a choreographer's preferences while generating music in real time. This work on compelling robot behaviors elucidates how teaching interfaces and interactions with robots in everyday settings can be appealing, efficient, and delightful.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
