Details
| Original language | English |
|---|---|
| Pages (from-to) | 377-403 |
| Number of pages | 27 |
| Journal | Autonomous robots |
| Volume | 47 |
| Issue number | 4 |
| Publication status | Published - Apr 2023 |
| Externally published | Yes |
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.
Keywords
- Haptics, Multimodal machine learning, Object manipulation, Robot perception, Sensor fusion, Tactile sensing
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
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In: Autonomous robots, Vol. 47, No. 4, 04.2023, p. 377-403.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Visuo-haptic object perception for robots
T2 - an overview
AU - Navarro-Guerrero, Nicolás
AU - Toprak, Sibel
AU - Josifovski, Josip
AU - Jamone, Lorenzo
N1 - Publisher Copyright: © 2023, The Author(s).
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
KW - Haptics
KW - Multimodal machine learning
KW - Object manipulation
KW - Robot perception
KW - Sensor fusion
KW - Tactile sensing
UR - http://www.scopus.com/inward/record.url?scp=85149892450&partnerID=8YFLogxK
U2 - 10.1007/s10514-023-10091-y
DO - 10.1007/s10514-023-10091-y
M3 - Article
AN - SCOPUS:85149892450
VL - 47
SP - 377
EP - 403
JO - Autonomous robots
JF - Autonomous robots
SN - 0929-5593
IS - 4
ER -