(Georgia Institute of Technology, 2013)
Arkin, Ronald C.; Dellaert, Frank; Srinivasa, Natesh; Kerwin, Ryan
Robot navigation already has many relatively efficient solutions: reactive control, simultaneous localization and mapping (SLAM), Rapidly-Exploring Random Trees (RRTs), etc. But many primates possess an additional inherent spatial reasoning capability:
mental rotation. Our research addresses the question of what role, if any, mental rotations
can play in enhancing existing robot navigational capabilities. To answer this question we explore the use of optical flow as a basis for extracting abstract representations of the world, comparing these representations with a goal state of similar
format and then iteratively providing a control signal to a robot to allow it to move in a direction consistent with
achieving that goal state. We study a range of transformation methods to implement the mental rotation component of the architecture, including correlation and matching based on cognitive studies. We also include a discussion of how
mental rotations may play a key role in understanding spatial advice giving, particularly from other members of the species, whether in map-based format, gestures, or other means of communication.
Results to date are presented on our
robotic platform.
(Georgia Institute of Technology, 2012)
Arkin, Ronald C.; Dellaert, Frank; Devassy, Joan
This paper describes ongoing research into the role of optic-flow derived
spatial representations and their relation to cognitive computational models
of mental rotation in primates, with the goal of producing effective and
unique autonomous robot navigational capabilities. A theoretical framework is
outlined based on a vectorial interlingua spanning perception, cognition and
motor control. Progress to date on its implementation within an autonomous robot
control architecture is presented.