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Yu Sun awarded a $334,823 NSF Grant to continue robotics research
October 8, 2019
USF Computer Science and Engineering Associate Professor and Associate Chair for Graduate
Affairs Yu Sun is the Principal Investigator (PI) for a $334,823 National Science
Foundation (NSF) grant to carry on fundamental research for the project titled “RI:
Small: Generalizing Learned Manipulation Skills to Unseen Situation by Balancing Uncertainties.”
According to the abstract, “Programming a robot to perform a manipulation task requires
a great deal of robotics knowledge and programming skills. Even for an expert, it
would take a significant amount of time to carefully craft an algorithm and a controller
for a robot to perform a particular task in a specified situation. Giving robots the
capability to learn from human demonstrations would remove the need for programming
and its cost. However, current approaches can only replicate the human's motion or
strategies of the learned task in its demonstrated situations but cannot transfer
them to unseen conditions. Since it is unrealistic to teach robots all tasks in every
possible situation, robots need to be able to generalize the manipulation skills learned
in one situation to unseen situations. The goal of the project is to develop a manipulation
skill generalization approach that takes into consideration both the variations in
the demonstrations and uncertainties in the unseen situation. By balancing them, the
robot can adapt the learned manipulation skills to new situations. The project advances
the effort of giving robots the capability to learn and continue their learning in
practice. With that capability, robots will be able to perform daily living tasks
and provide needed help to people with disabilities and seniors without the cost of
programming. The project will also produce new teaching materials for robotics courses
and train both graduate and undergraduate students.
The main idea of the project is that the robot should learn from the demonstration
data in the form of a broader probability representation rather than an optimized
but narrowed fitting, so that it is easy to incorporate the probability-based learning
from demonstration with the predictions in unseen situations. The project has four
objects: learning motion distribution inference model from demonstrations; learning
the distributions of two manipulation estimated outcomes and desired outcomes; generating
an optimal action by incorporating both motion distributions and outcome distributions;
and evaluating the approach on real physical system for daily manipulation tasks.
The project not only gives robots a capability to learn and generalize their skills
in practice, but also produces new tools for research and applications involving making
a decision based on experiences and predictable outcomes.” This project’s duration
is from September 1, 2019 to August 31, 2022.