Grasping Objects From the Floor in Assistive Robotics: Real World Implications and Lessons Learned

Puente, Paloma de la; Fischinger, David; Bajones, Markus; Wolf, Daniel; Vincze, Markus

doi:10.1109/access.2019.2938366

Cited by 9 publications

(8 citation statements)

References 31 publications

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“…Besides, a low standard deviation is indicated in both cases that is 0.86 and 1.14. In Table. 5 the event based method performance and the number of force adjustments is recorded for Objects (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11). The average slip measure from the overall objects handled is 1.4 mm and gives a low standard deviation 0.77 is observed.…”

Section: Slip Detection and Suppression Experiments And Resultsmentioning

confidence: 99%

“…With the emergence of Industry 4.0 [1] and increased use of service robots in several application domains [2], there is a growing demand for advanced perception capabilities in robotic systems to especially tackle the uncertainties occurring during physical tasks. Most of today's robots are equipped with parallel grippers or sophisticated hands which allows them to perform simple grasping to dexterous manipulation tasks [3] in both structured and unstructured environment.…”

Section: Introductionmentioning

confidence: 99%

“…A class of objects handled by the Baxter robot using event-based finger vision system: In column 1, objects(1)(2)(3)(4)(5)(6)(7)(8)(9)(10) caged by the robotic gripper is presented in a row order; Column 2 illustrates the associated feature-based slip detection and intelligent control action to suppress slip while lifting, manipulating and placing; Column 3 shows the initial slip instance while lifting the caged object in an event frame. The online method enables the slip detector and fuzzy controller to adapt and handle objects of different shape and size.…”

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confidence: 99%

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Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation

et al. 2020

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Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise and object feature events in real-time to improve slip detection and suppression. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under uncertainty and system for high-performance precision manipulation are presented. We also propose a slip metric to evaluate such performance quantitatively. For a class of objects, results indicate that the system can effectively detect incipient slip events at a sampling rate of 2kHz (∆t = 500µs) and suppress them before a gross slip occurs. The event-based approach holds promises to high precision manipulation task requirement in industrial manufacturing and household services.

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Section: Slip Detection and Suppression Experiments And Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation

et al. 2020

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“…Work in 31 proposed a novel grasping framework for sorting bottles in a complex environment, exploiting the Region Proposal Network (RPN) for object recognition and pose estimation 32 . Moreover, an intelligent grasping approach was proposed in 33 for picking objects from the floor using the method of highly accumulated features (HAF).…”

Section: Standard Vision-based Robotic Grasping Systemmentioning

confidence: 99%

Efficient Event-Based Robotic Grasping Perception using Hyperdimensional Computing

Hassan,

Zou,

Chen

et al. 2023

Preprint

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Grasping is Fundamental in various robotic applications, particularly within industrial contexts. Accurate inference of object properties is a crucial step toward enhancing grasping quality. Dynamic and Active Vision Sensors (DAVIS), increasingly utilized for robotic grasping, offer superior energy efficiency, lower latency, and higher temporal resolution than traditional cameras. However, the data they generate can be complex and noisy, necessitating substantial pre-processing. In response to these challenges, we introduce GraspHD, an innovative end-to-end algorithm that leverages brain-inspired hyperdimensional computing (HDC) to learn about the size and hardness of objects and estimate the grasping force. This novel approach circumvents the need for resource-intensive pre-processing steps, capitalizing on the simplicity and inherent parallelism of HDC operations. Our comprehensive analysis reveals that GraspHD surpasses state-of-the-art approaches in terms of overall classification accuracy. We have also implemented GraspHD on an FPGA to evaluate system efficiency. The results demonstrate that GraspHD operates at a speed 10x faster and offers an energy efficiency 26 times higher than existing learning algorithms, all while maintaining robust performance in noisy environments. These findings underscore the significant potential of GraspHD as a more efficient and effective solution for real-time robotic grasping applications.

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“…GiraffPlus robot benefits from a network of sensors to monitor the activities in the home environment, placed in and around the home, as well as on the body of the elderly to extract high-level activities from sensor data [28], [29]. Furthermore, Puente and colleagues report a system enabling a mobile robot to autonomously pick-up objects a human is pointing at from the floor [30], and their proposed system is tested in real apartments with elderly people using the second prototype of the Hobbit robot mentioned in [25].…”

Section: B Telerobotic Technology Applied On Assistive Carementioning

confidence: 99%

Teleoperation of Collaborative Robot for Remote Dementia Care in Home Environments

Yang

Zhou

et al. 2020

IEEE J. Transl. Eng. Health Med.

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As a senile chronic, progressive and currently incurable disease, dementia has an enormous impact on society and life quality of the elderly. The development of teleoperation technology has changed the traditional way of care delivery and brought a variety of novel applications for dementia care. In this paper, a telerobotic system is presented which gives the caregivers the capability of assisting dementia elderly remotely. The proposed system is composed of a dual-arm collaborative robot (YuMi) and a wearable motion capture device. The communication architecture is achieved by the robot operation system (ROS). The position-orientation data of the operator's hand are obtained and used to control the YuMi robot. Besides, a path-constrained mapping method is designed for motion trajectory tracking between the robot and the operator in the progress of teleoperation. Meanwhile, corresponding experiments are conducted to verify the performance of the trajectory tracking using the path-constrained mapping method. Results show that the position tracking deviation between the trajectory of the operator and the robot measured by dynamic time warping distance is 1.05 mm at the sampling frequency of 7.5 Hz. Moreover, the practicability of the proposed system was verified by teleoperating the YuMi robot to pick up a medicine bottle and further demonstrated by assisting an elderly woman in picking up a cup remotely. The proposed telerobotic system has potential utility for improving the life quality of dementia elderly and the care effect of their caregivers.

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Grasping Objects From the Floor in Assistive Robotics: Real World Implications and Lessons Learned

Cited by 9 publications

References 31 publications

Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation

Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation

Efficient Event-Based Robotic Grasping Perception using Hyperdimensional Computing

Teleoperation of Collaborative Robot for Remote Dementia Care in Home Environments

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