I am currently an assistant professor at Northeastern University. Please visit my new website.

My Ph.D. dissertation is about learning the state of the world for mobile-manipulation robots such as the Willow Garage PR2. Mobile-manipulation robots performing service tasks in human-centric indoor environments have long been a dream for developers of autonomous agents. Tasks such as cooking and cleaning typically involve interaction with the environment, hence robots need to know relevant aspects of their spatial surroundings. However, this information is rarely given a priori, and even if it is, the state of the world inevitably changes over time. Additionally, most information about the world is irrelevant to any particular task at hand.

Mobile manipulation robots therefore need to continuously perform the task of state estimation, using perceptual information to maintain a representation of the state, and its uncertainty, of task-relevant aspects of the world. By definition, mobile-manipulation robots are capable of moving in and interacting with the world. Hence, at the very least, such robots need to know about the physical occupancy of space and potential targets of interaction (i.e., objects). In my thesis, I propose a representation based on objects, their `semantic' attributes (task-relevant properties such as type and pose), and their geometric realizations in the physical world.

Learning the State of the World: Object-based World Modeling for Mobile-Manipulation Robots
Lawson L.S. Wong.
MIT EECS Ph.D. Dissertation, 2016.
[pdf]
MIT EECS Ph.D. Thesis Proposal, 2014.
[pdf]

Object-based World Modeling in Semi-Static Envrionments with Dependent Dirichlet Process Mixtures
Lawson L.S. Wong, Thanard Kurutach, Tomás Lozano-Pérez.
Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2016.
[pdf]

Searching for Physical Objects in Partially Known Environments
Xinkun Nie, Lawson L.S. Wong, Leslie Pack Kaelbling.
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2016.
[pdf]

Data Association for Semantic World Modeling from Partial Views
Lawson L.S. Wong, Leslie Pack Kaelbling, Tomás Lozano-Pérez.
The International Journal of Robotics Research (IJRR), 2015.
[pdf (preprint)]

Not Seeing is Also Believing: Combining Object and Metric Spatial Information
Lawson L.S. Wong, Leslie Pack Kaelbling, Tomás Lozano-Pérez.
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2014.
[pdf] [mp4]

Data Association for Semantic World Modeling from Partial Views
Lawson L.S. Wong, Leslie Pack Kaelbling, Tomás Lozano-Pérez.
Proceedings of the International Symposium on Robotics Research (ISRR), 2013.
[pdf]

Manipulation-based Active Search for Occluded Objects
Lawson L.S. Wong, Leslie Pack Kaelbling, Tomás Lozano-Pérez.
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2013.
[pdf] [mp4]

Collision-free State Estimation.
Lawson L.S. Wong, Leslie Pack Kaelbling, Tomás Lozano-Pérez.
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2012.
[pdf]

Undergraduate: 2005-2009

During my undergraduate years at Stanford, I worked on the Stanford Artificial Intelligence Robot (STAIR) project, under the Perception/Manipulation group. My main work was on teaching STAIR how to grasp objects that it has not seen before by learning visual (2-D and 3-D) features of successful and unsuccessful grasp examples; this culminated in my undergraduate honors thesis. I was supervised by Ashutosh Saxena and advised by Andrew Ng.

Robotic Grasping on the Stanford Artificial Intelligence Robot.
Lawson L.S. Wong.
Undergraduate Computer Science Honors Thesis, 2008.
(Ben Wegbreit Prize for Best Computer Science Honors Thesis)
[pdf]

Learning Grasp Strategies with Partial Shape Information.
Ashutosh Saxena, Lawson L.S. Wong, Andrew Y. Ng.
Proceedings of the AAAI Conference on Artificial Intelligence, 2008.
[pdf]

Learning to Select Robotic Grasps Using Vision on the Stanford Artificial Intelligence Robot.
Lawson Wong.
Stanford Undergraduate Research Journal (SURJ), 2008.
[pdf]

A Vision-based System for Grasping Novel Objects in Cluttered Environments.
Ashutosh Saxena, Lawson Wong, Morgan Quigley, Andrew Y. Ng.
Proceedings of the International Symposium on Robotics Research (ISRR), 2007.
[pdf]

Chinese University of Hong Kong at TRECVID 2006: Shot Boundary Detection and Video Search.
Steven C.H. Hoi, Lawson L.S. Wong, and Albert Lyu.
Proceedings of TREC Video Retrieval Evaluation Workshop, 2006.
[pdf]