Lessons Learnt from Scenario-Based Integration

Hawes, Nick; Zillich, Michael

doi:10.1007/978-3-642-11694-0_11

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Cognitive robotics is still in its beginning with many fundamental difficulties when it comes to autonomous behaviors and human interaction in fully unstructured environments [4], [5]. To reach industrial use within a reasonable number of years, our approach is to do full scale prototyping to confront scientific claims with real application settings.…”

Section: Approach and Methodologymentioning

confidence: 99%

On the integration of skilled robot motions for productivity in manufacturing

Björkelund

Edstrom

Haage

et al. 2011

2011 IEEE International Symposium on Assembly and Manufacturing (ISAM)

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Robots used in manufacturing today are tailored to their tasks by system integration based on expert knowledge concerning both production and machine control. For upcoming new generations of even more flexible robot solutions, in applications such as dexterous assembly, the robot setup and programming gets even more challenging. Reuse of solutions in terms of parameters, controls, process tuning, and of software modules in general then gets increasingly important.There has been valuable progress within reuse of automation solutions when machines comply with standards and behave according to nominal models. However, more flexible robots with sensor-based manipulation skills and cognitive functions for human interaction are far too complex to manage, and solutions are rarely reusable since knowledge is either implicit in imperative software or not captured in machine readable form.We propose techniques that build on existing knowledge by converting structured data into an RDF-based knowledge base. By enhancements of industrial control systems and available engineering tools, such knowledge can be gradually extended as part of the interaction during the definition of the robot task.

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Section: Approach and Methodologymentioning

confidence: 99%

On the integration of skilled robot motions for productivity in manufacturing

Björkelund

Edstrom

Haage

et al. 2011

2011 IEEE International Symposium on Assembly and Manufacturing (ISAM)

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“…Our paper is, of course, only a single data point in this endeavor. But if our community starts characterizing robotic systems along the proposed axes, thereby making design choices transparent and comparable, we might move towards a scientific theory of system building [2,20,27].…”

Section: Introductionmentioning

confidence: 99%

Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems

Eppner¹,

Höfer²,

Jonschkowski³

et al.

Robotics: Science and Systems XII

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Abstract-We describe the winning entry to the Amazon Picking Challenge. From the experience of building this system and competing in the Amazon Picking Challenge, we derive several conclusions: 1) We suggest to characterize robotic system building along four key aspects, each of them spanning a spectrum of solutions-modularity vs. integration, generality vs. assumptions, computation vs. embodiment, and planning vs. feedback. 2) To understand which region of each spectrum most adequately addresses which robotic problem, we must explore the full spectrum of possible approaches. To achieve this, our community should agree on key aspects that characterize the solution space of robotic systems. 3) For manipulation problems in unstructured environments, certain regions of each spectrum match the problem most adequately, and should be exploited further. This is supported by the fact that our solution deviated from the majority of the other challenge entries along each of the spectra.

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Lessons Learnt from Scenario-Based Integration

Cited by 4 publications

References 2 publications

On the integration of skilled robot motions for productivity in manufacturing

On the integration of skilled robot motions for productivity in manufacturing

Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems

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