AI News, Kris Hauser

Boosting

Our approach is capable of learning different types of models (Markov Logic Networks, Relational Dependency networks as well recently succesful Relational Logistic Regression), handling modeling of hidden data, learning with preferences from humans, scaling with large amounts of data by approximate counting and modeling temporal data.

RI Seminar: Kris Hauser : Beyond Geometric Path Planning

Beyond Geometric Path Planning: Paradigms and algorithms for modern robotics Kris Hauser Associate Professor, Duke University February 03, 2017 Abstract ...

Kris Hauser

RI Seminar : Brenna D. Argall : Human Autonomy through Robotics Autonomy

Brenna D. Argall Assistant Professor of Physical Medicine & Rehabilitation, Northwestern University Abstract It is a paradox that often the more severe a person's ...

RI Seminar: Russ Tedrake: Robust motion planning for walking robots and robotic birds

Robust motion planning for walking robots and robotic birds Russ Tedrake Associate Professor, MIT October 28, 2011 Abstract Building robots that can walk or ...

RI Seminar: Peter Stone : Robot Skill Learning: From the Real World to Simulation and Back

Peter Stone David Bruton, Jr. Centennial Professor, The University of Texas at Austin Abstract For autonomous robots to operate in the open, dynamically ...

RI Seminar : Sven Koenig: Progress on Multi-Robot Path Finding

Sven Koenig Professor, Computer Science Department, University of Southern California (USC) Abstract Teams of robots often have to assign target locations ...

ICAPS 2016: Sampling-based motion planning and its combination with task planning (part 1)

ICAPS 2016 -- Summer School Presentation Title: Sampling-based motion planning and its combination with task planning (part 1) Author: Mark Moll.