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Bio-inspired robot perception coupled with robot-modeled human perception

arXiv 2109.00097 ↗First posted 31 Aug 2021cs.RO cs.CVcited by 0

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Abstract (arXiv)

My overarching research goal is to provide robots with perceptional abilities that allow interactions with humans in a human-like manner. To develop these perceptional abilities, I believe that it is useful to study the principles of the human visual system. I use these principles to develop new computer vision algorithms and validate their effectiveness in intelligent robotic systems. I am enthusiastic about this approach as it offers the dual benefit of uncovering principles inherent in the human visual system, as well as applying these principles to its artificial counterpart. Fig. 1 contains a depiction of my research.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 31 Aug 2021.

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11 of 23 references.

References · 11

  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  2. 2019Event-based Vision: A Surveycited by 1,010
  3. 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
  4. 2019High Speed and High Dynamic Range Video with an Event Cameracited by 344
  5. 2021Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognitioncited by 236
  6. 2016Eye Tracking for Everyonecited by 155
  7. 2017MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimationcited by 118
  8. 2016Revisiting Active Perceptioncited by 90
  9. 2021Where is your place, Visual Place Recognition?cited by 82
  10. 2020Event-based visual place recognition with ensembles of temporal windowscited by 35
  11. 2020Fast and Robust Bio-inspired Teach and Repeat Navigationcited by 13

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