Towards lifelong assistive robotics: A tight coupling between object perception and manipulation

Kasaei, S. Hamidreza; Oliveira, Miguel; Lim, Gi Hyun; Lopes, Luís Seabra; Tomé, Ana Maria

doi:10.1016/j.neucom.2018.02.066

Cited by 48 publications

(13 citation statements)

References 41 publications

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“…Finally, this information was combined with the position of the eyes to manipulate the robot arm. Moreover, Kasaei et al in [102] proposed the incremental multiple-object recognition and localization (IMORL) algorithm for the JACO 2 robot to offer aid to disabled people to pick up objects. First, they extract the point clouds employing Kinect.…”

Section: Algorithms Used For Object Recognitionmentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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Section: Algorithms Used For Object Recognitionmentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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“…In the last decade, various research groups have made substantial progress towards the development of learning approaches which support online and incremental object category learning [18,17,9,19,25]. In such systems, object representation plays a prominent role since its output is used in both learning and recognition phases.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, the authors proposed a cognitive robotic architecture to create a concurrent 3D object category learning and recognition in an interactive and open-ended manner. Kasaei et al [17] proposed a naive Bayes learning approach with a Bag-of-Words object representation to acquire and refine object category models in open-ended fashion. Faulhammer et al [9] presented a system which allows a mobile robot to autonomously detect, model and recognize objects in everyday environments.…”

Section: Related Workmentioning

confidence: 99%

“…2) Results: We have conducted an extensive comparison with five open-ended 3D object category learning and recognition approaches, including RACE [25], BoW [17] using a L 2 based nearest neighbour classifier, Open-ended LDA, which is a modified version of the standard online LDA [13], Local-LDA [19] and GOOD [18]. Furthermore, since our approach with MobileNet-v2, VGG16-fc1 and ResNet50 demonstrated a good performance in previous round of evaluation, we have included all of them in this round of evaluation.…”

Section: B Open-ended Evaluationmentioning

confidence: 99%

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OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains

Kasaei¹

2019

Preprint

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Service robots are expected to be more autonomous and efficiently work in human-centric environments. For this type of robots, open-ended object recognition is a challenging task due to the high demand for two essential capabilities: (i) the accurate and real-time response, and (ii) the ability to learn new object categories from very few examples on-site. These capabilities are required for such robots since no matter how extensive the training data used for batch learning, the robot might be faced with an unknown object when operating in everyday environments. In this work, we present OrthographicNet, a deep transfer learning based approach, for 3D object recognition in open-ended domains. In particular, OrthographicNet generates a rotation and scale invariant global feature for a given object, enabling to recognize the same or similar objects seen from different perspectives. Experimental results show that our approach yields significant improvements over the previous state-of-theart approaches concerning scalability, memory usage and object recognition performance. Regarding real-time performance, two real-world demonstrations validate the promising performance of the proposed architecture. Moreover, our approach demonstrates the capability of learning from very few training examples in a real-world setting.

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“…Another important aspect is the additional assistance given to the user in the performance of the assigned task; focused, for instance, on the possibility of providing an amputee with the capability of performing bimanual tasks [6]. Besides, the necessity of interacting with the environment requires of vision systems to recognise the working place and provide a proper manipulation of the products [7].…”

Section: Introductionmentioning

confidence: 99%

Vision-driven collaborative robotic grasping system tele-operated by surface electromyography

Úbeda¹,

Zapata-Impata²,

Méndez³

et al. 2018

Preprint

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This paper presents a system that merges computer vision and surface electromyography techniques to carry out grasping tasks. To perform this, the vision-driven system is used to compute pre-grasping poses of the robotic system based on the analysis of tridimensional object features. Then, the human operator can correct the pre-grasping pose of the robot using surface electromyographic signals from the forearm during wrist flexion and extension. Weak wrist flexions and extensions allow a fine adjustment of the robotic system to grasp the object and finally, when the operator considers that the grasping position is optimal, a strong flexion is performed to initiate the grasping of the object. The system has been tested with several subjects to check its performance showing a grasping accuracy of around 95% of the attempted grasps which increases by around a 9% the grasping accuracy of previous experiments in which electromyographic control was not implemented.

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Towards lifelong assistive robotics: A tight coupling between object perception and manipulation

Cited by 48 publications

References 41 publications

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains

Vision-driven collaborative robotic grasping system tele-operated by surface electromyography

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