Organizer: Shenzhen Institute of Artificial Intelligence and Robotics for Society
Chair: Tin Lun Lam
Speaker: Sethu Vijayakumar
Topic: From Automation to Autonomy: Advances in Machine Learning driving Next-gen Robotics
The next generation of robots are going to work much more closely with humans, other robots and interact significantly with the environment around it. As a result, the key paradigms are shifting from isolated decision making systems to one that involves shared control — with significant autonomy devolved to the robot platform; and end-users in the loop making only high level decisions.
This talk will briefly introduce powerful machine learning technologies ranging from robust multi-modal sensing, shared representations, scalable real-time learning and adaptation and compliant actuation that are enabling us to reap the benefits of increased autonomy while still feeling securely in control.
This also raises some fundamental questions: while the robots are ready to share control, what is the optimal trade-off between autonomy and control that we are comfortable with? Domains where this debate is relevant include unmanned space exploration, self-driving cars, offshore asset inspection & maintenance, shared manufacturing, exoskeletons/prosthetics for rehabilitation as well as smart cities to list a few. I will end by highlighting the start of an exciting collaborative project between The University of Edinburgh and CUHK-AIRS on Shared Autonomy for Human-Robot Collaborative Teams and introduce the strand leads working on the project.