A roadmap to progress measurement science in robot dexterity and manipulation

Falco, Joe; Marvel, Jeremy A.; Messina, Elena R.

doi:10.6028/nist.ir.7993

Cited by 13 publications

(9 citation statements)

References 5 publications

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“…Hand exoskeletons that could benefit from industrial gripper test methods are being embedded in an astronaut's glove (Favetto et al, 2012) and used as hand exercise devices (Sarakoglou et al, 2007). The aforementioned roadmap (Falco et al, 2014) also includes dexterous robot arms, proposing fewer complex performance metrics than for dexterous grippers, e.g., reachable volume (i.e., the positions and orientations that an arm can achieve within the workspace), operational space (i.e., the positions and orientations in which the arm and/or hand can effectively perform the required operation), confined space access, and grasping objects while in motion.…”

Section: Test Methodsmentioning

confidence: 99%

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Cross-industry standard test method developments: from manufacturing to wearable robots

Bostelman

Messina

Foufou

2017

Frontiers Inf Technol Electronic Eng

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Manufacturing robotics is moving towards human-robot collaboration with light duty robots being used side by side with workers. Similarly, exoskeletons that are both passive (spring and counterbalance forces) and active (motor forces) are worn by humans and used to move body parts. Exoskeletons are also called 'wearable robots' when they are actively controlled using a computer and integrated sensing. Safety standards now allow, through risk assessment, both manufacturing and wearable robots to be used. However, performance standards for both systems are still lacking. Ongoing research to develop standard test methods to assess the performance of manufacturing robots and emergency response robots can inspire similar test methods for exoskeletons. This paper describes recent research on performance standards for manufacturing robots as well as search and rescue robots. It also discusses how the performance of wearable robots could benefit from using the same test methods.

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Section: Test Methodsmentioning

confidence: 99%

“…Grasping is another area in which performance test methods could be considered. A proposed roadmap for dexterous manipulation (Falco et al, 2014) includes impact areas focused on several aspects of dexterous arm and hand performance, including sensing, motion, control, and applications.…”

Section: Graspingmentioning

confidence: 99%

Cross-industry standard test method developments: from manufacturing to wearable robots

Bostelman

Messina

Foufou

2017

Frontiers Inf Technol Electronic Eng

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“…National Institute of Standards and Technology (NIST) workshop on dexterous manipulation highlighted tactile sensing as an important enabler for autonomous robotics [16]. In this connection, the research on artificial tactile sensing has been flourishing in recent decades [10].…”

Section: Slip and Texture Detectionmentioning

confidence: 99%

Deep Vibro-Tactile Perception for Simultaneous Texture Identification, Slip Detection, and Speed Estimation

Massalim

Kappassov

Varol

2020

Sensors

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Autonomous dexterous manipulation relies on the ability to recognize an object and detect its slippage. Dynamic tactile signals are important for object recognition and slip detection. An object can be identified based on the acquired signals generated at contact points during tactile interaction. The use of vibrotactile sensors can increase the accuracy of texture recognition and preempt the slippage of a grasped object. In this work, we present a Deep Learning (DL) based method for the simultaneous texture recognition and slip detection. The method detects non-slip and slip events, the velocity, and discriminate textures—all within 17 ms. We evaluate the method for three objects grasped using an industrial gripper with accelerometers installed on its fingertips. A comparative analysis of convolutional neural networks (CNNs), feed-forward neural networks, and long short-term memory networks confirmed that deep CNNs have a higher generalization accuracy. We also evaluated the performance of the highest accuracy method for different signal bandwidths, which showed that a bandwidth of 125 Hz is enough to classify textures with 80% accuracy.

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“…They discuss the definition, classification, scope, and limitations of many performance measures for manipulation and include an extensive bibliography. Manufacturing manipulation and grasping dexterity measures based on an industry-focused workshop are detailed in [53,54].…”

Section: Relevant Prior Work In Performance Metricsmentioning

confidence: 99%

Measuring and Representing the Performance of Manufacturing Assembly Robots

Shneier¹,

Messina²,

Schlenoff³

et al. 2015

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With the growth of robotic technology, there is a need for performance measures to characterize and compare robots and to help determine which features are most suited to a particular application. Robot systems are complex and involve a wide range of features and performance characteristics whose importance differs depending on the application domain. This paper describes a set of assembly performance measures associated with the different features along with an exploration of how one could represent this information. It organizes the assembly measures in reference to a taxonomy of assembly skills and tasks. Arranging the performance measures in this way will simplify the task of selecting a particular robot system for an assembly task by helping focus on those aspects of the task that are most critical.

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A roadmap to progress measurement science in robot dexterity and manipulation

Cited by 13 publications

References 5 publications

Cross-industry standard test method developments: from manufacturing to wearable robots

Cross-industry standard test method developments: from manufacturing to wearable robots

Deep Vibro-Tactile Perception for Simultaneous Texture Identification, Slip Detection, and Speed Estimation

Measuring and Representing the Performance of Manufacturing Assembly Robots

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