Multimodal perception for dexterous manipulation

Cao, Guanqun; Luo, Shan

doi:10.1016/b978-0-32-390445-2.00010-6

Cited by 4 publications

(1 citation statement)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The collected tactile pictures are then used to recreate a refined shape. Cao and Luo [85] discussed a multimodal perception for dexterous manipulation through visuo-tactile cross-modal learning and attention-based spatiotemporal tactile texture recognition. A visuo-tactile servoing control framework in ref.…”

Section: Visuo-tactile Servoingmentioning

confidence: 99%

Review on human‐like robot manipulation using dexterous hands

Sampath

Wang

Wu³

et al. 2023

Cognitive Comp and Systems

View full text Add to dashboard Cite

In recent years, human hand‐based robotic hands or dexterous hands have gained attention due to their enormous capabilities of handling soft materials compared to traditional grippers. Back in the earlier days, the development of a hand model close to that of a human was an impossible task but with the advancements made in technology, dexterous hands with three, four or five‐fingered robotic hands have been developed to mimic human hand nature. However, human‐like manipulation of dexterous hands to this date remains a challenge. Thus, this review focuses on (a) the history and motivation behind the development of dexterous hands, (b) a brief overview of the available multi‐fingered hands, and (c) learning‐based methods such as traditional and data‐driven learning methods for manipulating dexterous hands. Additionally, it discusses the challenges faced in terms of the manipulation of multi‐fingered or dexterous hands.

show abstract

Section: Visuo-tactile Servoingmentioning

confidence: 99%

Review on human‐like robot manipulation using dexterous hands

Sampath

Wang

Wu³

et al. 2023

Cognitive Comp and Systems

View full text Add to dashboard Cite

show abstract

Refined feature enhancement network for object detection

Li,

Dong

2024

Complex Intell. Syst.

View full text Add to dashboard Cite

Convolutional neural networks-based object detection techniques have achieved positive performances. However, due to the limitations of local receptive field, some existing object detection methods cannot effectively capture global information in feature extraction phases, and thus lead to unsatisfactory detection performance. Moreover, the feature information extracted by the backbone network may be redundant. To alleviate these problems, in this paper we propose a refined feature enhancement network (RFENet) for object detection. Specifically, we first propose a feature enhancement module (FEM) to capture more global and local information from feature maps with certain long-range dependencies. We further propose a multibranch dilated attention mechanism (MDAM) to refine the extracted features in a weighted form, which can select more important spatial and channel information and broaden the receptive field of the network. Finally, we validate RFENet on MS-COCO2017, PASCAL VOC2012, and PASCAL VOC07+12 datasets, respectively. Compared to the baseline network, our RFENet improves by 2.4 AP on MS-COCO2017 dataset, 3.4 mAP on PASCAL VOC2012 dataset, and 2.7 mAP on PASCAL VOC07+12 dataset. Extensive experiments show that our RFENet can perform competitively on different datasets. The code is available at https://github.com/object9detection/RFENet.

show abstract