Check out my recent videos: 


ICRA 2013: we are organizing a workshop on: Interactive Perception. More soon...

IEEE/RAS: I was a guest editor of the Robotics and Automation Magazine special issue on mobile manipulation. Check it out!




Interactive Perception of Unknown Articulated Objects

Autonomous manipulation in unstructured environments requires the following perceptual capabilities:

  1. Object Segmentation
  2. Object Tracking
  3. Kinematic Modeling

In my research, I developed the above perceptual capabilities for modeling planar articulated objects and general 3D articulated objects.
In addition, I proposed a relational reinforcement learning framework for gathering, generalizing and transferring manipulation expertise. 

This algorithm is computationally efficient, handles occlusion, and depends on little object motion.
I conducted experiments with everyday objects on a robotic manipulation platform equipped with either a camera or an RGB-D sensor. The results demonstrate the robustness of the proposed method to lighting conditions, object appearance, size, structure, and configuration.

This work represents a paradigm shift in the structure of robotic algorithm.
It breaks the traditional boundaries between action and perception: deliberate interactions are used to reveal information that would otherwise remain hidden or difficult to interpret.

Modeling 3D Articulated Objects
Modeling Planar Articulated Objects
Relational Reinforcement Learning of Manipulation Expertise
  Segmenting 3D Articulated Objects