Visuo-haptic object perception for robots: an overview

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Autonomous Robots Pub Date : 2023-03-14 DOI:10.1007/s10514-023-10091-y

Nicolás Navarro-Guerrero, Sibel Toprak, Josip Josifovski, Lorenzo Jamone

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引用次数: 5

Abstract

The object perception capabilities of humans are impressive, and this becomes even more evident when trying to develop solutions with a similar proficiency in autonomous robots. While there have been notable advancements in the technologies for artificial vision and touch, the effective integration of these two sensory modalities in robotic applications still needs to be improved, and several open challenges exist. Taking inspiration from how humans combine visual and haptic perception to perceive object properties and drive the execution of manual tasks, this article summarises the current state of the art of visuo-haptic object perception in robots. Firstly, the biological basis of human multimodal object perception is outlined. Then, the latest advances in sensing technologies and data collection strategies for robots are discussed. Next, an overview of the main computational techniques is presented, highlighting the main challenges of multimodal machine learning and presenting a few representative articles in the areas of robotic object recognition, peripersonal space representation and manipulation. Finally, informed by the latest advancements and open challenges, this article outlines promising new research directions.

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机器人视觉触觉对象感知研究综述

人类的物体感知能力令人印象深刻，当试图开发出在自主机器人中具有类似熟练程度的解决方案时，这一点变得更加明显。尽管人工视觉和触摸技术取得了显著进步，但这两种感觉模式在机器人应用中的有效集成仍需改进，并且存在一些悬而未决的挑战。本文从人类如何结合视觉和触觉感知来感知物体属性并驱动手动任务的执行中获得灵感，总结了机器人视觉-触觉物体感知的现状。首先，概述了人类多模态物体感知的生物学基础。然后，讨论了机器人传感技术和数据采集策略的最新进展。接下来，概述了主要的计算技术，强调了多模式机器学习的主要挑战，并介绍了机器人对象识别、个人周围空间表示和操作领域的一些代表性文章。最后，根据最新进展和面临的挑战，本文概述了有前景的新研究方向。

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来源期刊

Autonomous Robots 工程技术-机器人学

CiteScore

7.90

自引率

5.70%

发文量

审稿时长

3 months

期刊介绍： Autonomous Robots reports on the theory and applications of robotic systems capable of some degree of self-sufficiency. It features papers that include performance data on actual robots in the real world. Coverage includes: control of autonomous robots · real-time vision · autonomous wheeled and tracked vehicles · legged vehicles · computational architectures for autonomous systems · distributed architectures for learning, control and adaptation · studies of autonomous robot systems · sensor fusion · theory of autonomous systems · terrain mapping and recognition · self-calibration and self-repair for robots · self-reproducing intelligent structures · genetic algorithms as models for robot development. The focus is on the ability to move and be self-sufficient, not on whether the system is an imitation of biology. Of course, biological models for robotic systems are of major interest to the journal since living systems are prototypes for autonomous behavior.