Framework for Assessing Robotic Dexterity within Flexible Manufacturing

Culleton, Mark; McGinn, Conor; Kelly, Kevin

doi:10.1007/s10846-017-0505-x

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…here, R i is the total return, s t corresponds to the step t when that state was visited, and N t denotes the number of times the s t state in a given step t has been visited. In order to establish a balance between computational efficiency and memory requirements, instead of using the arithmetic mean as in Equation (18), the incremental mean is more commonly used, and it is expressed as follows:…”

Section: Learningmentioning

confidence: 99%

“…This integration of perception and primitive actions defined as services in ROS allows the robot to execute the bagging task defined in this paper. 1…”

Section: Bagging Task Implementationmentioning

confidence: 99%

“…However, RL agents are commonly trained in simulation, which brings more challenges when implementing the agent in realworld tasks [16]. One challenge is that before simulating the bag, the model must be as similar as possible to the real object [17,18]. On the other hand, when switching from simulation to real world, the agent must deal with occlusions, incomplete information, and noises.…”

Section: Introductionmentioning

confidence: 99%

“…Robots with human-level dexterity that can handle deformable objects may encourage smoother integration of robots in daily and industrial activities [1]. In practice, daily activities depend on more than manipulating rigid objects.…”

Section: Introductionmentioning

confidence: 99%

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Learning to bag with a simulation‐free reinforcement learning framework for robots

Munguia‐Galeano,

Zhu,

Hernández

et al. 2024

IET Cyber-Syst and Robotics

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Bagging is an essential skill that humans perform in their daily activities. However, deformable objects, such as bags, are complex for robots to manipulate. A learning‐based framework that enables robots to learn bagging is presented. The novelty of this framework is its ability to learn and perform bagging without relying on simulations. The learning process is accomplished through a reinforcement learning (RL) algorithm introduced and designed to find the best grasping points of the bag based on a set of compact state representations. The framework utilises a set of primitive actions and represents the task in five states. In our experiments, the framework reached 60% and 80% success rates after around 3 h of training in the real world when starting the bagging task from folded and unfolded states, respectively. Finally, the authors test the trained RL model with eight more bags of different sizes to evaluate its generalisability.

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Section: Learningmentioning

confidence: 99%

“…This integration of perception and primitive actions defined as services in ROS allows the robot to execute the bagging task defined in this paper. 1…”

Section: Bagging Task Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning to bag with a simulation‐free reinforcement learning framework for robots

Munguia‐Galeano,

Zhu,

Hernández

et al. 2024

IET Cyber-Syst and Robotics

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show abstract

“…The evaluation of tasks for HRC automation is different from conventional robotic automation as safety implications also need to be considered. Different techniques have been described in the literature to evaluate product complexity for cobot automation [ [72] , [73] , [74] , [75] ].…”

Section: Simulating Hrc Human-robot Assembly System For Ventilator Prmentioning

confidence: 99%

Reconfiguring and ramping-up ventilator production in the face of COVID-19: Can robots help?

Malik

Masood

Kousar

2021

Journal of Manufacturing Systems

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Highlights As the COVID-19 pandemic expands, causing a shortage of medical supplies, a key piece of equipment has been ventilator. The manufacturing systems of today are designed for mass production with minimum product variability at competitive cost Besides high potential of cobotic automation, their application in assembly is limited Collaborative robots can form reconfigurable manufacturing systems and can ramp up production. Modularization, plug-&-play hardware and digital twins are key enablers for fast (re) configuration of cobots.

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A general cost model to assess the implementation of collaborative robots in assembly processes

Barravecchia

Mastrogiacomo

Franceschini

2023

Int J Adv Manuf Technol

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In assembly processes, collaborative robots (cobots) can provide valuable support to improve production performance (assembly time, product quality, worker wellbeing). However, there is a lack of models capable of evaluating cobot deployment and driving decision-makers to choose the most cost-effective assembly configuration. This paper tries to address this gap by proposing a novel cost model to evaluate and predict assembly costs. The model allows a practical and straightforward comparison of different potential assembly configurations in order to guide the selection towards the most effective one. The proposed cost model considers several cost dimensions, including manufacturing, setup, prospective, retrospective, product quality and wellbeing costs. The cost estimation also considers learning effects on assembly time and quality, particularly relevant in low-volume and mass customised productions. Three real manufacturing case studies accompany the description of the model.

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Framework for Assessing Robotic Dexterity within Flexible Manufacturing

Cited by 9 publications

References 30 publications

Learning to bag with a simulation‐free reinforcement learning framework for robots

Learning to bag with a simulation‐free reinforcement learning framework for robots

Reconfiguring and ramping-up ventilator production in the face of COVID-19: Can robots help?

A general cost model to assess the implementation of collaborative robots in assembly processes

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