Join us on April 19 for the Grand Challenges in Robotics: Dexterity an interactive webinar discussing the challenges, roles, and future trends in robot dexterity.

Grand Challenges in Robotics: Dexterity

Conversation 5 on Robot Dexterity: Machine Learning for Robot Manipulation and Dexterity

Wednesday, April 19, 12-1 PM CT

Machine learning has the potential to provide the multimodal representations and adaptive skills needed to bring robot manipulation and dexterity into a wide range of new and challenging application domains. To achieve these goals, robots will need to utilize a variety of different information sources, including demonstrations, simulations, interactive perception, and trial-and-error learning. Our panel of experts will discuss the various learning challenges posed by robot manipulation, the roles of different types of learning in achieving dexterity, and future trends that will lay the foundation for ubiquitous robot manipulation.

Moderator: Oliver Kroemer, Carnegie Mellon University

Panelists: Rika Antonova, Stanford University; Chelsea Finn, Stanford University and Google Brain; Animesh Garg, University of Toronto and NVIDIA; Andy Zeng, Google

Register: https://northwestern.zoom.us/webinar/register/WN_pDnM13mhQeGstnIVWXWZHA

For more information on the Grand Challenges in Robotics: Dexterity series: https://robotics.northwestern.edu/grand-challenges/