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Link to original content: https://doi.org/10.1007/s10015-014-0153-1
Learning to navigate in a virtual world using optic flow and stereo disparity signals | Artificial Life and Robotics Skip to main content
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Learning to navigate in a virtual world using optic flow and stereo disparity signals

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Abstract

Navigating in a complex world is challenging in that the rich, real environment provides a very large number of sensory states that can immediately precede a collision. Biological organisms such as rodents are able to solve this problem, effortlessly navigating in closed spaces by encoding in neural representations distance toward walls or obstacles for a given direction. This paper presents a method that can be used by virtual (simulated) or robotic agents, which uses states similar to neural representations to learn collision avoidance. Unlike other approaches, our reinforcement learning approach uses a small number of states defined by discretized distances along three constant directions. These distances are estimated either from optic flow or binocular stereo information. Parameterized templates for optic flow or disparity information are compared against the input flow or input disparity to estimate these distances. Simulations in a virtual environment show learning of collision avoidance. Our results show that learning with only stereo information is superior to learning with only optic flow information. Our work motivates the usage of abstract state descriptions for the learning of visual navigation. Future work will focus on the fusion of optic flow and stereo information, and transferring these models to robotic platforms.

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Acknowledgments

All authors are supported in part by CELEST, an NSF Science of Learning Center (SMA-0835976). FR acknowledges support from the Office of Naval Research (ONR N00014-11-1-0535 and ONR MURI N00014-10-1-0936). MV acknowledges support from the National Aeronautics and Space Administration (NASA NNX12AH31G).

Author information

Authors and Affiliations

  1. Center for Computational Neuroscience and Neural Technology (CompNet) at Boston University, 677 Beacon Street, Boston, MA, 02215, USA

    Florian Raudies & Massimiliano Versace

  2. Department of Electrical and Computer Engineering at Boston University, 8 St. Mary Street, Boston, MA, 02215, USA

    Schuyler Eldridge & Ajay Joshi

Authors
  1. Florian Raudies
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  2. Schuyler Eldridge
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  3. Ajay Joshi
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  4. Massimiliano Versace
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Corresponding author

Correspondence to Florian Raudies.

Appendix

Appendix

We provide pseudo-code for the bio-inspired proposed models that estimate distances of walls from stereo or flow information (Tables 3, 4, 5).

Table 3 Pseudo-code for the algorithm stereo-based template model
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Table 4 Pseudo algorithm for flow-based template model
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Table 5 Q-learning algorithm with ε-Greedy action selection
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Raudies, F., Eldridge, S., Joshi, A. et al. Learning to navigate in a virtual world using optic flow and stereo disparity signals. Artif Life Robotics 19, 157–169 (2014). https://doi.org/10.1007/s10015-014-0153-1

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  • DOI: https://doi.org/10.1007/s10015-014-0153-1

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