Special Track on Physically Grounded AI (PGAI)
Robotics, computer vision, activity recognition, and the many other disciplines where computers interface to physical environments have proven to be a major source of inspiration and crucial new insights into artificial intelligence. Physical grounding provides ready access to a challenging, real problems of perception and action with rich source of data and the related stochastic effects. Further, applications of physically grounded AI have enormous potential to improve research, learning, entertainment, commerce, and society as a whole.
This special track includes research papers on AI techniques, systems and concepts applied to physically grounded systems including activity recognition, robotics, and machine perception.
Papers will either describe related research or clearly explain how the work addresses problems in physically embodied agents, opportunities, or issues underlying such systems. Relevant topics include the following:
- AI for Robotics
- Activity Recognition
- Computer Vision
- Intelligence and Perception for Human-Robot interaction
- Machine Learning applied to robotics, vision, and other activities grounded in the real world
- Machine Learning for control and decision making
- Motion planning
- Prediction and planning for transportation
- Sensor Networks
- Three-Dimensional Machine Perception
Special Track Cochairs
- J. Andrew Bagnell (Carnegie Mellon University)
- Martial Hebert (Carnegie Mellon University)
- Odest Chadwicke Jenkins (Brown University)
The Special Track on Physically Grounded AI call for papers is available as a printable PDF document and also in html.