Michael Pardowitz

About

Michael Pardowitz is a robotics researcher whose work centers on robot learning, human-robot interaction, and intelligent manipulation — areas where he has made meaningful contributions to the long-standing vision of capable robot assistants operating alongside humans. His most influential work focuses on enabling robots to learn tasks incrementally from human demonstration, a challenge he addressed through hierarchical task representations and sequential constraint learning. His 2007 paper on incremental task learning from user demonstrations, vocal comments, and past experiences has garnered 142 citations, reflecting its significant influence on the programming-by-demonstration (PbD) community. Complementing this, his 2006 work on building hierarchical manipulation knowledge bases earned 85 citations, establishing an important framework for reasoning about and reorganizing learned task knowledge. Pardowitz also explored biologically and psychologically inspired approaches, drawing on Piagetian developmental theory for lifelong robot learning and Gestalt principles for action segmentation. His later research extended into tactile sensing, including entropy-based dimensionality reduction and object identification from tactile data. Across his body of work, Pardowitz consistently bridged cognitive science, machine learning, and practical robotics, offering students and researchers a rich foundation for understanding how robots can be taught to understand and adapt to complex, human-centered environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Incremental Learning of Tasks From User Demonstrations, Past Experiences, and Vocal Comments

   142 citations · 2007
2. 2
   Towards Cognitive Robots: Building Hierarchical Task Representations of Manipulations from Human Demonstration

   85 citations · 2006
3. 3
   Learning sequential constraints of tasks from user demonstrations

   27 citations · 2006
4. 4
   Open source real-time control software for the Kuka light weight robot

   23 citations · 2010
5. 5
   Gestalt-based action segmentation for robot task learning

   19 citations · 2008
6. 6
   Towards life-long learning in household robots: The Piagetian approach

   18 citations · 2007
7. 7
   Identifying Relevant Tactile Features for Object Identification

   17 citations · 2012
8. 8
   Using entropy for dimension reduction of tactile data

   12 citations · 2009
9. 9
   Learning repetitive robot programs from demonstrations using version space algebra

   11 citations · 2007
10. 10
    Automatic robot programming from learned abstract task knowledge

    11 citations · 2007

Key Collaborators

Contact & Links