Associate Professor

Department of Computer Science

Department of Electrical and Computer Engineering

Grainger College of Engineering

University of Illinois at Urbana-Champaign

Bio

Kris Hauser is an Associate Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He received his PhD in Computer Science from Stanford University in 2008, bachelor's degrees in Computer Science and Mathematics from UC Berkeley in 2003, and worked as a postdoctoral fellow at UC Berkeley. He then joined the faculty at Indiana University from 2009-2014, where he started the Intelligent Motion Lab, and then joined the faculty of Duke University from 2014-2019. Prof. Hauser is a recipient of a Stanford Graduate Fellowship, Siebel Scholar Fellowship, Best Paper Award at IEEE International Conference on Humanoid Robots 2015, the NSF CAREER award, and two Amazon Research Awards. He also works as a consultant for Google's autonomous driving company, Waymo.

CV...

Research

Research interests include robot motion planning and control, semiautonomous robots, and integrating perception and planning, as well as applications to intelligent vehicles, robotic manipulation, robot-assisted medicine, and legged locomotion.

More information can be found on the Intelligent Motion Lab website...

Selected Publications

• F. Wang and K. Hauser. Robot Packing with Known Items and Nondeterministic Arrival Order. Robotics: Science and Systems (RSS), June 2019. Best Paper Award Nominee
• M. Draelos, P. Ortiz, R. Qian, B. Keller, K. Hauser, A. Kuo, and J. Izatt. Automatic Optical Coherence Tomography Imaging of Stationary and Moving Eyes with a Robotically-Aligned Scanner. IEEE International Conference on Robotics and Automation, May, 2019. ICRA video
• G. Tang and K. Hauser. Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts. IEEE International Conference on Robotics and Automation, May, 2019. (A prior version was available in ArXiv, 2018.)
• F. Wang and K. Hauser. In-hand Object Scanning via RGB-D Video Segmentation. IEEE International Conference on Robotics and Automation, May, 2019.
• F. Wang and K. Hauser. Stable Bin Packing of Non-convex 3D Objects with a Robot Manipulator. IEEE International Conference on Robotics and Automation, May, 2019. (A prior version was available in ArXiv, 2018.)
• Y. Zhu, L. Abdulmajeid, and K. Hauser. Data-driven Approach for Fast Simulation of Robot Locomotion on Granular Media. IEEE International Conference on Robotics and Automation, May, 2019. ICRA video
• K. Hauser. Semi-Infinite Programming for Trajectory Optimization with Nonconvex Obstacles. Workshop on the Algorithmic Foundations of Robotics (WAFR), December 2018.
• K. Hauser. Bayesian Tactile Exploration for Compliant Docking with Uncertain Shapes. Robotics: Science and Systems, June 2018.
• K. Hauser and S. Emmons. Global Redundancy Resolution via Continuous Pseudoinversion of the Forward Kinematic Map. IEEE Transactions on Automation Science and Engineering, 15(3): 932 - 944, March 2018.
• F. Wang, G. Chen, and K. Hauser. Robot Button Pressing In Human Environments . IEEE Intl Conf. on Robotics and Automation (ICRA), 2018. ICRA video
• M. Draelos, B. Keller, G. Tang, A. Kuo, K. Hauser, and J. Izatt.Real-Time Image-Guided Cooperative Robotic Assist Device for Deep Anterior Lamellar Keratoplasty . IEEE Intl Conf. on Robotics and Automation (ICRA), 2018. ICRA video
• S. Wang and K. Hauser. Realization of a Real-time Optimal Control Strategy to Stabilize a Falling Humanoid with Hand Contact . IEEE Intl Conf. on Robotics and Automation (ICRA), 2018. ICRA video
• K. Hauser and Y. Zhou. Asymptotically Optimal Planning by Feasible Kinodynamic Planning in State-Cost Space. IEEE Transactions of Robotics, 32(6): 1431-1443, 2016. Also in arXiv:1505.04098 [cs.RO], 2015.
• O. Ramos and K. Hauser. Generalizations of the Capture Point to Nonlinear Center of Mass Paths and Uneven Terrain. IEEE-RAS Int'l Conference on Humanoid Robots, November, 2015. Best Paper Award
• K. Hauser. The Minimum Constraint Removal Problem with Three Robotics Applications. International Journal of Robotics Research, 33(1):5-17, January, 2014. doi: 10.1177/0278364913507795
• K. Hauser. Robust Contact Generation for Robot Simulation with Unstructured Meshes. International Symposium on Robotics Research, 2013.
• K. Hauser. Fast Interpolation and Time-Optimization on Implicit Contact Submanifolds. In proceedings of Robotics: Science and Systems (RSS), Berlin, Germany, June 2013.

Full list...

Information for Prospective Students

• PhD candidates. I generally accept applications for highly qualified PhD students on a yearly basis through the UIUC CS or ECE departments. If you email me your CV and I encourage you to apply, then I consider you a qualified candidate. If you do not receive a response, this means that your background probably isn't a good fit for my lab, and your chances of getting accepted are small.

• Master's students. I do not admit Master's students into UIUC, so please do not bother contacting me about admissions. If you are already a Master's student at UIUC, my policy for accepting students into my lab is as follows:

  • I do not take on Master's students in their first semester, except under rare circumstances where the student is exceptionally qualified (e.g., worked in state-of-the-art R&D at a robotics company or research lab).
  • In your first semester, you should receive A's or A+'s in relevant coursework (robotics, AI, optimization, and machine learning classes). Your GPA should be 3.8 or above, overall.
  • The first semester you work in the lab is an evaluation semester. To become a full-fledged member of the lab, you must demonstrate your ability to comprehend state-of-the-art research and contribute to a research project that is likely to lead to a high-quality publication.

• Undergraduate students. I frequently involve undergraduates in my research, both as independent study students and summer interns. My lab also participates in competitions, so I also take on students who are interested in contributing to a team effort. Currently, we are involved in the ANA Avatar XPRIZE challenge. My minimum guidelines are that you have a 3.7 GPA, relevant coursework, and can contribute at least 10 hours per week to research.

  Note: because a large fraction of my funding comes from NSF, US citizens are much more likely to receive paid summer internships.

Information for Reviewers

Reviewers are the unsung heroes of the academic enterprise. Although I would prefer that reviewers be fairly compensated by journals and conferences, that doesn't seem like it will happen any time soon. As a small consolation, my standing policy is that if you perform a review for me, I will happily treat you to to a drink or lunch as a token of thanks for your service. Meet me in person (e.g., at a conference) to redeem this offer!

Blog posts and other musings

• Should society regulate the irrationality of human nature? (2/22/2019)
• The 10 biggest technical hurdles in robotics (4/27/2017)
• A Convergence Result for the Baby Feeding Problem (1/19/2009). (incomplete; I'll have to finish this at some point!)

Teaching

• Spring 2019. ECE 489/MEMS 555.06: Advanced Robotic System Design, Duke University
  
• Fall 2018. ECE 383/MEMS 442/MEMS 555: Introduction to Robotics and Automation, Duke University
  
• Spring 2018. ECE 590: Motion Planning and Optimal Control, Duke University
  
• Fall 2017. ECE 383 / MEMS 442: Intro to Robotics and Automation
  
• Spring 2017. CS 270: Introduction to Artificial Intelligence, Duke University; ECE 590: Amazon Robotics Challenge, Duke University
  
• Fall 2016. ECE 383 / MEMS 442: Intro to Robotics and Automation
  
• Spring 2016. ECE 490 / MEMS 555: Advanced Robot System Design
  
• Fall 2015. ECE 383 / MEMS 442: Intro to Robotics and Automation
  
• Spring 2015. ECE 590: Intelligent Robot Motion
  
• Spring 2014. I400/I590/B659: Intelligent Robots
  
• Fall 2013. B351 Introduction to Artificial Intelligence and Computer Simulation
  
• Spring 2012. B553 Optimization and Learning Algorithms
  

Notes