Interactive Perception of Unknown Articulated Objects
Autonomous manipulation in unstructured environments requires the following perceptual capabilities:
- Object Segmentation
- Object Tracking
- 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 | |||
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| Segmenting 3D Articulated Objects | |||
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