Seminar: Applying User-Centered Design to Interactive Machine Learning
Seminar by Samantha Krening
Georgia Institute of Technology
The widespread integration of robotics into everyday life requires significant improvement in the underlying machine learning agents to make them more accessible, customizable, and intuitive for ordinary individuals to interact with. As part of a larger field of interactive machine learning, this work aims to create intelligent agents that can easily be taught by individuals with no specialized training, using an intuitive teaching method such as critique, demonstrations, or explanations. It is imperative for researchers to be aware of how design decisions affect the human’s experience because individuals who experience frustration while interacting with a robot are unlikely to continue or repeat the interaction in the future. Instead of asking how to train a person to use software, this research asks how to design software agents so they can be easily trained by people.
When creating a robotic system, designers must make numerous decisions concerning the mobility, morphology, intelligence, and interaction of the robot. This work focuses on the design of the interaction between a human and intelligent agent, specifically an agent that learns from a human’s verbal instructions. Most research concerning interaction algorithms aims to improve the traditional ML metrics of the agent, such as cumulative reward and training time, while neglecting the human experience. My work demonstrates that decisions made during the design of interaction algorithms impact the human’s satisfaction with the ML agent. I propose a series of design recommendations that researchers should consider when creating IML algorithms.
Samantha Krening received her B.S. and M.S. degrees in Aerospace Engineering from the University of Colorado at Boulder, where she emphasized in astrodynamics and control. She then worked for NASA’s Jet Propulsion Laboratory for three years, where she was a Guidance & Control Engineer for the Cassini spacecraft orbiting Saturn. Samantha is currently finishing her Ph.D. in Robotics from the Georgia Institute of Technology, where she has been part of the Cognitive Engineering Center. Her doctoral work in Interactive Machine Learning combines human factors and reinforcement learning to create agents that can learn from non-expert human instruction.