Forum for Artificial Intelligence

Forum for Artificial Intelligence

[ About FAI   |   Upcoming talks   |   Past talks ]

The Forum for Artificial Intelligence meets every other week (or so) to discuss scientific, philosophical, and cultural issues in artificial intelligence. Both technical research topics and broader inter-disciplinary aspects of AI are covered, and all are welcome to attend!

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The current schedule is also available as a Google Calendar or alternatively in iCal format.

[ Upcoming talks ]

Mon, October 5
GDC 6.302
Anca Dragan
UC Berkeley
Towards Robotics Algorithms that Reason about People

Monday, October 5, 2015, 11:00AM

GDC 6.302

Towards Robotics Algorithms that Reason about People

Anca Dragan   [homepage]

UC Berkeley

The goal of my research is to enable robots to work with, around, and in support of people, autonomously producing behavior that reasons about both their function and their interaction with humans. I aim to develop a formal understanding of interaction that leads to algorithms which are informed by mathematical models of how humans interact with robots, enabling generalization across robot morphologies and interaction modalities.

In this talk, I will focus on one specific instance of this agenda: autonomously generating motion for coordination during human-robot collaborative manipulation. Most motion in robotics is solely functional: industrial robots move to package parts, vacuuming robots move to suck dust, and personal robots move to clean up a dirty table. This type of motion is ideal when the robot is performing a task in isolation. Collaboration, however, does not happen in isolation, and demands that we move beyond solely functional motion. In collaboration, the robot's motion has an observer, watching and interpreting the motion – inferring the robot's intent from the motion, and anticipating the robot's motion based on its intent.

My work integrates a mathematical model of these inferences into motion planning, so that the robot can generate motion that matches people's expectations and clearly conveys its intent. In doing so, I draw on action interpretation theory, Bayesian inference, constrained trajectory optimization, and interactive learning. The resulting motion not only leads to more efficient collaboration, but also increases the fluency of the interaction as defined through both objective and subjective measures.

About the speaker:

Anca Dragan is a new Assistant Professor at UC Berkeley's EECS Department. She completed her PhD in Robotics at Carnegie Mellon. She was born in Romania and received her B.Sc. in Computer Science from Jacobs University in Germany in 2009. Her research lies at the intersection of robotics, machine learning, and human-computer interaction: she works on algorithms that enable robots to seamlessly work with, around, and in support of people. Anca's research and her outreach activities with children have been recognized by the Intel Fellowship and by scholarships from Siebel, the Dan David Prize, and Google Anita Borg.

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