Special Track on Physically Grounded AI
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.
The special track invited research papers on AI techniques, systems and concepts applied to physically grounded systems including activity recognition, robotics, and machine perception.
Papers 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:
- 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
- Drew Bagnell
(Carnegie Mellon University) - Wolfram Burgard
(University of Freiburg) - Irfan Essa
(Georgia Institute of Technology)
The Special Track on Physically Grounded AI call for papers is also available as a printable PDF document