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Virtual Human Vision
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Virtual Human Vision icon
This project is concerned with understanding the management of visually guided behavior in the face of dynamic environments and multiple goals. This project has two aims. The first is to address engineering questions concerning the control of embodied agents. The second is to address the related question of how humans handle visuo-motor tasks.

Nathan Sprague (Ph. D. Thesis Work) Dana Ballard (Thesis Advisor)
Virtual Human Vision figure
Nathan Sprague
The virtual human performing a navigation task. The task is to stay on the sidewalk while avoiding the blue obstacles and picking up purple litter. The colored rays show simulated fixations. Blue rays are sidewalk fixations, red rays are obstacle fixations, and green rays are pickup fixations.

This project is concerned with understanding the management of visually guided behavior in the face of dynamic environments and multiple goals. This project has two aims. The first is to address engineering questions concerning the control of embodied agents. The second is to address the related question of how humans handle visuo-motor tasks. The research platform is a graphical humanoid that must navigate through a realistically rendered urban environment. The virtual human's control architecture is built on the premise that complex control problems can be handled by sequencing and combining simple visuo-motor routines that each handle a single well defined task. In the robotics community this approach, referred to as behavior based control, has gained wide acceptance. A challenge for behavior based control is that embodied agents have inherent resource restrictions; eyes can only look in one direction at a time, and limbs can only be used for a single task at once. It is an open question how best to fairly distribute these limited perceptual and physical resources between concurrently active routines with potentially conflicting demands. We address the resource allocation question from a decision theoretic perspective: resources are distributed preferentially to those routines that stand to benefit the most. Reinforcement learning algorithms are used to construct a mapping from action choices to expected return.
http://www.cs.rochester.edu/~sprague/research.html
virtual human, nathan sprague, dana ballard

Sprague_A_Visual_Control_Architecture_HR_2001 Sprague_Multiple_Goal_RL_IJCAI_2003

Active (April 15, 2003)