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Learning Visual Representations for Perception-Action Systems




We discuss vision as a sensory modality for systems that interact flexibly with uncontrolled environments. Instead of trying to build a generic vision system that produces task-independent representations, we argue in favor of task-specific, learnable representations. This concept is illustrated by two examples of our own work. First, 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, additionally 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 grasp density. Thus, object detection in a novel scene simultaneously produces suitable grasping options.

Author(s): Piater, J. and Jodogne, S. and Detry, R. and Kraft, D. and Krüger, N. and Kroemer, O. and Peters, J.
Journal: International Journal of Robotics Research
Volume: 30
Number (issue): 3
Pages: 294-307
Year: 2011
Month: February
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1177/0278364910382464
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Learning Visual Representations for Perception-Action Systems},
  author = {Piater, J. and Jodogne, S. and Detry, R. and Kraft, D. and Kr{\"u}ger, N. and Kroemer, O. and Peters, J.},
  journal = {International Journal of Robotics Research},
  volume = {30},
  number = {3},
  pages = {294-307},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2011},
  month_numeric = {2}