Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Learning object-specific grasp affordance densities
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
A Probabilistic Framework for 3D Visual Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning the affordances of tools using a behavior-grounded approach
Proceedings of the 2006 international conference on Towards affordance-based robot control
Temporal accumulation of oriented visual features
Journal of Visual Communication and Image Representation
Learning visual representations for perception-action systems
International Journal of Robotics Research
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We describe a system for autonomous learning of visual object representations and their grasp affordances on a robot-vision system. It segments objects by grasping and moving 3D scene features, and creates probabilistic visual representations for object detection, recognition and pose estimation, which are then augmented by continuous characterizations of grasp affordances generated through biased, random exploration. Thus, based on a careful balance of generic prior knowledge encoded in (1) the embodiment of the system, (2) a vision system extracting structurally rich information from stereo image sequences as well as (3) a number of built-in behavioral modules on the one hand, and autonomous exploration on the other hand, the system is able to generate object and grasping knowledge through interaction with its environment.