Embodied Localization in Visually-guided Walk of Humanoid Robots

Chame, Hendry Ferreira; Chevallereau, Christine

doi:10.5220/0005063001650174

Cited by 3 publications

References 20 publications

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Sensory-Motor Anticipation and Local Information Fusion for Reliable Humanoid Approach

Chame

Chevallereau

2016

New Trends in Medical and Service Robots

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The possibility of developing increasingly sophisticated robots, and the availability of cloud-connected resources, have boosted the interest in the study of real world applications of service robotics. However, in order to operate under natural or less structured conditions, and given the information processing bottleneck and the reactivity required for a secure execution of the task, it is desirable that the agent can exploit more efficiently the local information available, so that being more autonomous, and relying less on remote computation. This study explores a strategy for obtaining reliable approach tasks. It considers the anticipation of perception, by taking into account the statistical regularities and the information redundancies induced in the sensorymotor coupling. From an initial perception of the object assisted by remote computation, contextual features are defined for capturing bodily sensations emerging in the task. The observations based on proprioceptive and visual data are fused in a Bayesian Network, which is in charge of assessing the saliency during the object approach, thus constituing a local discriminative processing of the object. The strategy proposed reduces dependency on context-free models of behavior, while providing an estimate on the degree of confidence in the progress of the task.

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Sensory-Motor Anticipation and Local Information Fusion for Reliable Humanoid Approach

Chame

Chevallereau

2016

New Trends in Medical and Service Robots

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Grounding humanoid visually guided walking: From action-independent to action-oriented knowledge

Chame

Chevallereau

2016

Information Sciences

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A Top-Down and Bottom-Up Visual Attention Model for Humanoid Object Approaching and Obstacle Avoidance

Chame¹,

Chevallereau²

2016

2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR)

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International audienceMost of the research on humanoid walk tasks has considered a global representation of the scene that frequently relies on external sensors. This is detrimental to the autonomy and the reactivity of the agent under unknown or changing scenarios. Ego-centric localization has been less explored, and the works considering on-board acquisitions have mostly dealt with tasks under controlled scenarios where the path to the object is cleared from obstacles. In this work a behavior-based control scheme is proposed, so the robot Nao can approach and position in relation to a given face of an object, while avoiding obstacles. For this, the solution relies on top-down (color-based) and bottom-up (optic-flow-based) visual features, and proprioceptive information registered on-board. The model is decentralized and exploits the emergent aspect of behavior from the independent contribution of a walk and a look-at task. An embodied visual encoding approach is proposed to support the arbitration between competing behavioral modes

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Embodied Localization in Visually-guided Walk of Humanoid Robots

Cited by 3 publications

References 20 publications

Sensory-Motor Anticipation and Local Information Fusion for Reliable Humanoid Approach

Sensory-Motor Anticipation and Local Information Fusion for Reliable Humanoid Approach

Grounding humanoid visually guided walking: From action-independent to action-oriented knowledge

A Top-Down and Bottom-Up Visual Attention Model for Humanoid Object Approaching and Obstacle Avoidance

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