My research is on the development of intelligent machines. This
includes work in object discovery, robot mapping, and activity
recognition. This builds on my research in robotics for methods that
bridge the divide between the representations that robots need for
intelligence (high-level concepts of space, objects and actions) and
the physical devices that robots use for interaction (low-level
sensors and motors).
Two important types of knowledge are predictions about possible future
experience, and controls that achieve an observable goal. A robot can
learn approximate value-functions to acquire knowledge
of these forms directly from its sensorimotor experience.
Good representations are required for effective learning and planning
on hard problems. A key question for representational development is
to gain both a formal and empirical understanding of how a robot can
develop better representations.
- Discovering Sensor Space: Constructing Spatial Embeddings That Explain Sensor Correlations
Joseph Modayil. International Conference on Development and Learning (ICDL 2010). pages 120--125.
[ pdf ]
By using RFID sensors to detect the sequence of objects an individual
is touching, computers can infer what activities they are performing.
This research has the potential to assist individuals with cognitive
impairments.
- Improving the recognition of interleaved activities
Joseph Modayil, Tongxin Bai, Henry Kautz. In Ubicomp 2008.
[ pdf]
- Integrating sensing and cueing for more effective activity reminders
Joseph Modayil, Rich Levinson, Craig Harman, David Halper, Henry Kautz. In the 2008 AAAI Fall Symposium on AI in Eldercare.
[ pdf]
My thesis work
examines how a robot can autonomously discover and use objects.
- Bootstrap learning for object discovery
Joseph Modayil and Benjamin Kuipers. In
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-04), pages 742--747.
[pdf]
[abstract]
[Earlier symposium version]
 
-
Autonomous shape model learning for object localization and recognition
Joseph Modayil and Benjamin Kuipers. In
IEEE International Conference on Robotics and Automaton (ICRA-06), pages 2991--2996.
[pdf] [abstract]
- Bootstrap learning of foundational representations
Benjamin Kuipers, Patrick Beeson, Joseph Modayil, and Jefferson Provost.
Connection Science, 18(2), June 2006, pages 145-158.
[pdf][abstract][Earlier workshop version]
- Where do actions come from? Autonomous robot learning of objects and actions
Joseph Modayil and Benjamin Kuipers.
In AAAI Spring Symposium Series 2007, Control Mechanisms for Spatial Knowledge Processing in Cognitive / Intelligent Systems.
[pdf][abstract]
- Autonomous Development of a Grounded Object Ontology by a Learning Robot
Joseph Modayil and Benjamin Kuipers.
In the Proceedings of the Twenty-Second Conference on
Artificial Intelligence (AAAI-07).
[pdf][abstract]
- Robot Developmental Learning of an Object Ontology Grounded in Sensorimotor Experience
Joseph Modayil. Doctoral dissertation, Computer Sciences Department, University of Texas at Austin.
[pdf][abstract]
- The initial development of object knowledge by a learning robot
Joseph Modayil and Benjamin Kuipers. Robotics and Autonomous Systems.
Volume 56, Issue 11, pages 879--890.
We have created a hybrid topological/metrical robot map builder.
Robots need maps to indicate which regions are safe for travel.
- Local metrical and global topological maps in the Hybrid Spatial Semantic Hierarchy
Benjamin Kuipers, Joseph Modayil, Patrick Beeson, Matt MacMahon, and Francesco Savelli. In
IEEE International Conference on Robotics and Automation (ICRA-04).
[pdf]
[abstract]
- Using the topological skeleton for scalable global metrical map-building
Joseph Modayil, Patrick Beeson and Benjamin Kuipers. In
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-04), pages 1530--1536.
[pdf]
[abstract]
 
- Building local safety maps for a wheelchair robot using vision and lasers
(Best student paper!)
Aniket Murarka, Joseph Modayil, and Benjamin Kuipers. In
Canadian Conference on Computer and Robot Vision (CRV-06).
[pdf]
[abstract]
- Integrating multiple representations of spatial knowledge for mapping, navigation, and communication
Patrick Beeson, Matt MacMahon, Joseph Modayil, Aniket Murarka, Benjamin Kuipers, and Brian Stankiewicz.
In the Proceedings of the Symposium on Interaction Challenges for Intelligent Assistants. AAAI Spring Symposium Series, March 2007
[pdf][abstract]
- Factoring the mapping problem: mobile robot map-building in the Hybrid Spatial Semantic Hierarchy
Patrick Beeson, Joseph Modayil, and Benjamin Kuipers. International Journal of Robotics Research, 2009.
(in press)
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