Learning from Demonstration and Correction via Multiple Modalities for a Humanoid Robot

Argall, Brenna; Billard, Aude

doi:10.1051/bioconf/20110100003

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Other sensors also use a small number of underlying transducers to recover richer information about the contact. For example, work in the ROBOSKIN project showed how to calibrate multiple piezocapacitive transducers [19], used them to recover a complete contact profile [20] using an analytic model of deformation, and finally used such information for manipulation learning tasks [21]. Our localization method is entirely data driven and makes no assumptions about the underlying properties of the medium, which could allow coverage of more complex geometric surfaces.…”

Section: Related Workmentioning

confidence: 99%

Touch Sensors with Overlapping Signals: Concept Investigation on Planar Sensors with Resistive or Optical Transduction

Piacenza,

Hannigan,

Baumgart

et al. 2018

Preprint

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Section: Related Workmentioning

confidence: 99%

Touch Sensors with Overlapping Signals: Concept Investigation on Planar Sensors with Resistive or Optical Transduction

Piacenza,

Hannigan,

Baumgart

et al. 2018

Preprint

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“…Other sensors also use a small number of underlying transducers to recover richer information about the contact. For example, work in the ROBOSKIN project showed how to calibrate multiple piezocapacitive transducers [9], used them to recover a complete contact profile [10] using an analytic model of deformation, and finally used such information for manipulation learning tasks [11]. Our localization method is entirely data driven and makes no assumptions about the underlying properties of the medium, which could allow coverage of more complex geometric surfaces.…”

Section: Related Workmentioning

confidence: 99%

Accurate contact localization and indentation depth prediction with an optics-based tactile sensor

Piacenza

Dang

Hannigan

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Traditional methods to achieve high localization accuracy with tactile sensors usually use a matrix of miniaturized individual sensors distributed on the area of interest. This approach usually comes at a price of increased complexity in fabrication and circuitry, and can be hard to adapt for non planar geometries. We propose to use low cost optic components mounted on the edges of the sensing area to measure how light traveling through an elastomer is affected by touch. Multiple light emitters and receivers provide us with a rich signal set that contains the necessary information to pinpoint both the location and depth of an indentation with high accuracy. We demonstrate sub-millimeter accuracy on location and depth on a 20mm by 20mm active sensing area. Our sensor provides high depth sensitivity as a result of two different modalities in how light is guided through our elastomer. This method results in a low cost, easy to manufacture sensor. We believe this approach can be adapted to cover non-planar surfaces, simplifying future integration in robot skin applications.

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Robot Learning from Human Teachers

Chernova¹,

Thomaz

2014

Synthesis Lectures on Artificial Intelligence and Machine Learning

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Learning from Demonstration and Correction via Multiple Modalities for a Humanoid Robot

Abstract: Abstract

Cited by 4 publications

References 14 publications

Touch Sensors with Overlapping Signals: Concept Investigation on Planar Sensors with Resistive or Optical Transduction

Touch Sensors with Overlapping Signals: Concept Investigation on Planar Sensors with Resistive or Optical Transduction

Accurate contact localization and indentation depth prediction with an optics-based tactile sensor

Robot Learning from Human Teachers

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