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Claudia Pérez D’Arpino - Learning How to Plan for Multi-Step Manipulation in Collaborative Robotics

Machine Learning and Friends Lunch
November 20
Computer Science Building, Room 150/151

Abstract:

The use of robots for complex manipulation tasks is
currently challenged by the limited ability of robots to construct a
rich representation of the activity at both the motion and tasks
levels in ways that are both functional and apt for human-supervised
execution. For instance, the operator of a remote robot would benefit
from planning assistance, as opposed to the currently used method of
joint-by-joint direct teleoperation. In manufacturing, robots are
increasingly expected to execute manipulation tasks in shared
workspace with humans, which requires the robot to be able to predict
the human actions and plan around these predictions. In both cases, it
is beneficial to deploy systems that are capable of learning skills
from observed demonstrations, as this would enable the application of
robotics by users without programming skills. However, previous work
on learning from demonstrations is limited in the range of tasks that
can be learned and generalized across different skills and different
robots. I this talk, I present C-LEARN, a method of learning from
demonstrations that supports the use of hard geometric constraints for
planning multi-step functional manipulation tasks with multiple end
effectors in quasi-static settings, and show the advantages of using
the method in a shared autonomy framework.

 

Bio:

Claudia Pérez D’Arpino is a PhD Candidate in the Electrical
Engineering and Computer Science Department at the Massachusetts
Institute of Technology, advised by Prof. Julie A. Shah in the
Interactive Robotics Group since 2012. She received her degrees in
Electronics Engineering (2008) and Masters in Mechatronics (2010) from
the Simon Bolivar University in Caracas, Venezuela, where she served
as Assistant Professor in the Electronics and Circuits Department
(2010-2012) with a focus on Robotics. She participated in the DARPA
Robotics Challenge with Team MIT (2012-2015). Her research at CSAIL
combines machine learning and planning techniques to empower humans
through the use of robotics and AI. Her PhD research centers in
enabling robots to learn and create strategies for multi-step
manipulation tasks by observing demonstrations, and develop efficient
methods for robots to employ these skills in collaboration with
humans, either for shared workspace collaboration, such as assembly in
manufacturing, or for remote robot control in shared autonomy, such as
emergency response scenarios.
Web:http://people.csail.mit.edu/cdarpino/