An Attention-Based Methodology for Context Identification and Exploitation in Autonomous Robots

By: Maria Alessandra Montironi

Advisor: Professor Harry Cheng

Every intelligent agent, human or artificial, heavily relies on its understanding of the current context, that is, on its situational awareness, to choose its next set of actions. Motivated by the often harmful outcomes of decisions made without appropriate situational awareness, a number of studies have been conducted in the field of ergonomics to understand how humans construct and utilize situational awareness when performing critical tasks. These findings constitute a solid base upon which computational methods that address the same problem in autonomous robots can be designed. In particular, when considering the issue in autonomous systems, three aspects need to be addressed and combined: perception, context classification, and context-dependent decision making.

This research work aims at addressing the problem of developing a comprehensive methodology for context identification and exploitation in autonomous robots by using an attention-based approach. In particular it investigates how the concepts of attention and surprise, traditionally used for salience detection when processing visual data, can be applied at different levels of a robotic architecture. To achieve this goal, first this work presents a probabilistic framework for attention-based extraction of high level hypotheses from sensor data. Then, a method is developed that extends the concept of attention beyond processing of sensor data to the process of inferring the current context the robot is acting in. Last, a number of methods are presented that show how the acquired information can be used by an autonomous robot for goal selection and dynamic behavior adaptation. The theory, algorithms, and software implementation have been validated through the two case studies of ball catching for robot soccer and surveillance. The performance of the methodology has been evaluated through simulated and hardware experiments.

Date(s) - 11/30/2017
1:00 pm - 2:00 pm

2130 Bainer - MAE Conference Room


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