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Conference Paper

Learning Visual Representations for Interactive Systems

MPS-Authors
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Piater,  J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Kroemer,  O.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Peters,  J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Piater, J., Jodogne, S., Detry, R., Kraft, D., Krüger, N., Kroemer, O., et al. (2011). Learning Visual Representations for Interactive Systems. In 14th International Symposium on Robotics Research (ISRR 2009) (pp. 399-416).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-4D66-C
Abstract
We describe two quite different methods for associating action parameters to visual percepts. Our RLVC algorithm performs reinforcement learning directly on the visual input space. To make this very large space manageable, RLVC interleaves the reinforcement learner with a supervised classification algorithm that seeks to split perceptual states so as to reduce perceptual aliasing. This results in an adaptive discretization of the perceptual space based on the presence or absence of visual features. Its extension RLJC also handles continuous action spaces. In contrast to the minimalistic visual representations produced by RLVC and RLJC, our second method learns structural object models for robust object detection and pose estimation by probabilistic inference. To these models, the method associates grasp experiences autonomously learned by trial and error. These experiences form a non-parametric representation of grasp success likelihoods over gripper poses, which we call a gra sp d ensi ty. Thus, object detection in a novel scene simultaneously produces suitable grasping options.