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Persistent and Shared Autonomy for Interactive Robotics

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Course Credit: 0.15 CEU, 1.5 PDH

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 sharing control — with significant autonomy devolved to the RAS systems; and end-users in the loop making only high level decisions.

The key questions is: while the robots are ready to share control, what is the optimal trade-off between autonomy and control that we are comfortable with?
This talk will look at technologies ranging from robust multi-modal sensing, shared representations, compliant actuation and real-time learning and adaptation that are enabling us to reap the benefits of increased autonomy while still feeling securely in control.

Domains where this debate is relevant NOW include self-driving cars, mining, shared manufacturing, exoskeletons for rehabilitation, active prosthetics, large scale scheduling (e.g. transport) systems as well as Oil and Gas exploration to list a few.

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 1 chapter

Course Chapters

  • 1Persistent and Shared Autonomy for Interactive Robotics - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.15 CEU credit1.5 PDH credits

Speakers

David Lane
Sethu Vijayakumar