Prospective Student Information
Information for New Students and Post-Docs interested in working with me.
I am always looking for bright and very motivated students graduate and undergraduate (and sometimes Post Docs) to work with me on exciting research projects.
While there are a variety of backgrounds that are appropriate for working in my lab, as strong a background in mathematics as possible is good, as is the ability to code real systems. Recently I have moved more towards robotics and thus any background with robot programming (such as ROS) is also helpful. Of course, general experience with computer vision and machine learning is also useful.
Here I outline some of the areas in which I would be very interesting in finding folks to collaborate with (updated Jan, 2014):
Human Action Understanding for Human-Robot Collaboration
This work is really a shift of my activity recognition work into the domain of robotics. In much of computer vision, activity recognition is generating a label: this video is an example of someone playing basketball. For a robot to react and interact, it needs to understand the action and be able to make predictions about human behavior. For example work see:
Hawkins, K., S. Bansal, N. Vo, and A.F. Bobick , “Modeling structured activity to support human-robot collaboration in the presence of task and sensor uncertainty,” IROS Workshop on Cognitive Robotics Systems, 2013
Hawkins, K., N. Vo, S. Bansal, and A.F. Bobick, “Probabilistic Human Action Prediction and Wait-Sensitive Planning for Responsive Human-Robot Collaboration,” IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2013
The set of possible actions and their outcomes with respect to the
robot and an object are referred to as affordances:
the “action possibilities” latent in the environment for a given agent. For example, a book with a
planar side affords the ability to push it and a handle attached to a coffee cup affords the ability to grasp it.
Traditionally, much work in robot planning presumes that the physics of the world and its objects can be
modeled, learned and simulated in sufficient detail such that the effect of an action in any context can be
robustly predicted. Under these assumptions the affordances of any object can be predicted through simulation.
However, it is currently an open question as to what extent this is possible. Even if possible, it will
certainly require extensive and exquisite sensing.
The notion of affordance based learning and action is that as
an alternative to the physics-complete planning system, a perception
driven, affordance-behavior approach allows for combining limited,
perceptually-derived geometrical information with experientially-learned
knowledge about the behavior of objects. The idea is that at any given
point in time, the robot knows about some set of objects and
experimental manipulations of those objects
whose outcomes have been recorded. Given some specific object and a task objective, the robot will determine
a plan of actions to perform based upon already learned affordances of similar objects.
For example work see:
Tucker Hermans, Fuxin Li, James M. Rehg, Aaron F. Bobick. "Learning Contact Locations for Pushing and Orienting Unknown Objects." IEEE-RAS International Conference on Humanoid Robotics (Humanoids), Atlanta, GA, USA, October 2013.
Tucker Hermans, Fuxin Li, James M. Rehg, Aaron F. Bobick. "Learning Stable Pushing Locations." IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EPIROB), Osaka, Japan, August 2013.
Human Activity Recognition
I still have an interest in activity recognition, but much less from a notion of labeling.
My group used to publish primarily in Computer Vision, especially in CVPR and ICCV. Recently, however, much more of my work is appearing in Robotics conferences including, ICRA, IROS, HRI and Humanoids.