Robot tasks for real-world applications typically involve multiple paths of execution, where the same task can be achieved in different ways. This poses challenges with respect to the representation and execution of such tasks, as enumerating all possible execution paths leads to combinatorial increases in the size of the representation. We present a novel robot control architecture that addresses these challenges. The architecture 1) provides an efficient, compact encoding of tasks with multiple paths of execution, 2) uses the same compact representation as the controller that the robot will use to achieve its goals, 3) allows the robot to dynamically decide which execution path to follow using an activation spreading mechanism that relies on environmental conditions, and 4) provides a mechanism for robustness to changes in the environment during the task execution. We validate our architecture using a humanoid PR2 robot, showing that the robot dynamically selects a path of execution based on the current state of the environment, and is robust to environmental changes.

Designing Collaborator Robots for Highly-Dynamic Multi-Human, Multi-Robot Teams, Office of Naval Research (ONR) PI: Monica Nicolescu, co-PI: David Feil-Seifer, Amount: $656,511, April 1, 2016 - March 31, 2019

DURIP: Humanoid Platforms for Human-Robot Collaboration, Office of Naval Research (ONR) PI: Monica Nicolescu, co-PI: David Feil-Seifer, Mircea Nicolescu, Amount: $312,000, May 1, 2014 - June 30, 2015