Encoding safe task achievement in humanoid robots and autonomous agents

Subramanian Ramamoorthy
Intelligent Robotics Lab
University of Texas at Austin

Abstract

How can a humanoid robot safely walk on uneven ground, without having prior models of its environment? How can an autonomous trading agent operate safely in an unknown random environment? I will show that it is possible to design such strategies using two components: (a) task encoding in terms of qualitative models and (b) techniques for learning, search and optimization to enable adaptation to the environment. Qualitative models ( i.e., simple abstractions whose phase space geometry may be used to specify spatial, temporal and dynamical constraints) enable principled analysis of safety and structural stability. Machine learning allows the agent to bind these abstractions to the more complex environment the agent actually interacts with. This allows the agent to deal effectively with imprecision and environmental variation. I will conclude with a brief discussion on how this methodology and representation enables new algorithms for behavior discovery and verification.


Subramanian Ramamoorthy is a doctoral candidate at the University of Texas at Austin. His research is conducted within the Intelligent Robotics Laboratory, under the supervision of Dr. Benjamin J. Kuipers. His research interests include bio-inspired robotics, autonomous agents, control in biology and algorithmic techniques in machine learning, geometry and topology. In addition to his academic experience, he has over seven years of industrial experience at National Instruments Corp. where he has been a part of various research and development groups including motion control, computer vision and dynamic simulation.