Benchmarking Protocols for Evaluating Small Parts Robotic Assembly Systems

Kimble, Kenneth; Wyk, Karl Van; Falco, Joe; Messina, Elena R.; Sun, Yu; Shibata, Masaaki; Uemura, Wataru; Yokokohji, Yasuyoshi

doi:10.1109/lra.2020.2965869

Cited by 59 publications

(40 citation statements)

References 14 publications

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“…The work in [7] utilizes Rubik's cube to assess the system's precision manipulation capabilities as well as its ability to conduct sequential manipulation. The work in [8] presents a benchmark for robotic assembly using a standard assembly board (which is available to purchase on demand) that includes tasks such as insertion, cable routing and screwing.…”

Section: Benchmarks With Testsmentioning

confidence: 99%

Guest Editorial: Introduction to the Special Issue on Benchmarking Protocols for Robotic Manipulation

Çallı

Dollar

Roa

et al. 2021

IEEE Robot. Autom. Lett.

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Section: Benchmarks With Testsmentioning

confidence: 99%

Guest Editorial: Introduction to the Special Issue on Benchmarking Protocols for Robotic Manipulation

Çallı

Dollar

Roa

et al. 2021

IEEE Robot. Autom. Lett.

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“…As such, there is a need to industry benchmarks as to what level of complexity versus cost corresponds to a break-even point for adoption of RD applications, in which an economic gain, i.e. profitability, is still foreseeable for RDbased re-manufacturing [161]. This margin of profit could be leveraged to justify a partial transfer of the responsibility to manufacturers in accepting additional costs for modifications in design and production process to facilitate disassembly [162].…”

Section: Sustainability and Circular Economy Considerationsmentioning

confidence: 99%

Industry 4.0 and Prospects of Circular Economy: A Survey of Robotic Assembly and Disassembly

Daneshmand¹,

Noroozi²,

Corneanu³

et al. 2021

Preprint

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Despite their contributions to the financial efficiency and environmental sustainability of industrial processes, robotic assembly and disassembly have been understudied in the existing literature. This is in contradiction to their importance in realizing the Fourth Industrial Revolution. More specifically, although most of the literature has extensively discussed how to optimally assemble or disassemble given products, the role of other factors has been overlooked. For example, the types of robots involved in implementing the sequence plans, which should ideally be taken into account throughout the whole chain consisting of design, assembly, disassembly and reassembly. Isolating the foregoing operations from the rest of the components of the relevant ecosystems may lead to erroneous inferences toward both the necessity and efficiency of the underlying procedures. In this paper we try to alleviate these shortcomings by comprehensively investigating the state-of-the-art in robotic assembly and disassembly. We consider and review various aspects of manufacturing and remanufacturing frameworks while particularly focusing on their desirability for supporting a circular economy.

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“…Previous arXiv:2103.11512v2 [cs.AI] 23 Mar 2021 attempts to demonstrate learned insertion tasks [1,2,3,4,5,6,7] have varied wildly in the task definition, action space, connector type, and sensor observations. Fortunately, the recently introduced NIST assembly benchmark [8] provides an externally defined set of representative industrial assembly tasks with clearly defined metrics.…”

Section: Introductionmentioning

confidence: 99%

Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study

Luo

Sushkov

Pevceviciute

et al. 2021

Robotics: Science and Systems XVII

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Over the past several years there has been a considerable research investment into learning-based approaches for tasks inspired by industrial manufacturing, but despite significant progress, these techniques have yet to be adopted by in the realworld. We argue that it is the prohibitively large design space for Deep Reinforcement Learning (DRL), rather than algorithmic limitations per se, that are truly responsible for this lack of adoption. Pushing these techniques into the industrial mainstream requires a paradigm which differs significantly from the academic mindset. In this paper we define criteria for industryoriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against results of a professional industrial integrator on the recently established NIST assembly benchmark. We explain the design choices, representing several years of investigation, which enabled our DRL system to consistently outperform the integrator's baseline in terms of both speed and reliability. Finally, we conclude with a competition between our DRL system and a human on a challenge task of insertion into a randomly moving target. This study suggests that DRL is capable of outperforming not only established engineered approaches, but the human motor system as well, and that there remains significant room for improvement. Videos can be found on our project website:https://sites.google.com/view/shield-nist.

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Benchmarking Protocols for Evaluating Small Parts Robotic Assembly Systems

Cited by 59 publications

References 14 publications

Guest Editorial: Introduction to the Special Issue on Benchmarking Protocols for Robotic Manipulation

Guest Editorial: Introduction to the Special Issue on Benchmarking Protocols for Robotic Manipulation

Industry 4.0 and Prospects of Circular Economy: A Survey of Robotic Assembly and Disassembly

Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study

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