Self-supervised learning of depth-based navigation affordances from haptic cues

Baleia, José; Santana, Pedro; Barata, José

doi:10.1109/icarsc.2014.6849777

Cited by 4 publications

(1 citation statement)

References 11 publications

(9 reference statements)

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“…This paper is an extended and improved version of a conference paper [6], and it is organised as follows. Section 3 describes the proposed system.…”

Section: Introductionmentioning

confidence: 99%

On Exploiting Haptic Cues for Self-Supervised Learning of Depth-Based Robot Navigation Affordances

Baleia

Santana

Barata

2015

J Intell Robot Syst

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This article presents a method for online learning of robot navigation a↵or-dances from spatiotemporally correlated haptic and depth cues. The method allows the robot to incrementally learn which objects present in the environment are actually traversable. This is a critical requirement for any wheeled robot performing in natural environments, in which the inability to discern vegetation from non-traversable obstacles frequently hampers terrain progression. A wheeled robot prototype was developed in order to experimentally validate the proposed method. The robot prototype obtains haptic and depth sensory feedback from a pan-tilt telescopic antenna and from a structured light sensor, respectively. With the presented method, the robot learns a mapping between objects' descriptors, given the range data provided by the sensor, and objects' sti↵ness, as estimated from the interaction between the antenna and the object. Learning confidence estimation is considered in order to progressively reduce the number of required physical interactions with acquainted objects. To raise the number of meaningful interactions per object under time pressure, the several segments of the object under analysis are prioritised according to a set of morphological criteria. Field trials show the ability of the robot to progressively learn which elements of the environment are traversable.

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“…This paper is an extended and improved version of a conference paper [6], and it is organised as follows. Section 3 describes the proposed system.…”

Section: Introductionmentioning

confidence: 99%

On Exploiting Haptic Cues for Self-Supervised Learning of Depth-Based Robot Navigation Affordances

Baleia

Santana

Barata

2015

J Intell Robot Syst

Self Cite

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A volumetric representation for obstacle detection in vegetated terrain

Lourenço

Marques

Santana

et al. 2014

2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)

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Computational models of affordance in robotics: a taxonomy and systematic classification

et al. 2017

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J. J. Gibson's concept of affordance, one of the central pillars of ecological psychology, is a truly remarkable idea that provides a concise theory of animal perception predicated on environmental interaction. It is thus not surprising that this idea has also found its way into robotics research as one of the underlying theories for action perception. The success of the theory in this regard has meant that existing research is both abundant and diffuse by virtue of the pursuit of multiple different paths and techniques with the common goal of enabling robots to learn, perceive and act upon affordances. Up until now there has existed no systematic investigation of existing work in this field. Motivated by this circumstance, in this article we begin by defining a taxonomy for computational models of affordances rooted in a comprehensive analysis of the most prominent theoretical ideas of import in the field. Subsequently, after performing a systematic literature review, we provide a classification of existing research within our proposed taxonomy. Finally, by both quantitatively and qualitatively assessing the data resulting from the classification process, we highlight gaps in the research terrain and outline open questions for the investigation of affordances in robotics that we believe will help inform future work, prioritize research goals, and potentially advance the field towards greater robot autonomy.Prepared using sagej.cls [Version: 2015/06/09 v1.01]

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Self-supervised learning of depth-based navigation affordances from haptic cues

Cited by 4 publications

References 11 publications

On Exploiting Haptic Cues for Self-Supervised Learning of Depth-Based Robot Navigation Affordances

On Exploiting Haptic Cues for Self-Supervised Learning of Depth-Based Robot Navigation Affordances

A volumetric representation for obstacle detection in vegetated terrain

Computational models of affordance in robotics: a taxonomy and systematic classification

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