Festo Didactic Industrial IoT Case Study | IoT ONE Digital Transformation Advisors

Case Studies

ANDOR

Analytics & Modeling(1)
- Machine Learning(1)

ANDOR

Discrete Manufacturing(1)
Logistics & Transportation(1)

ANDOR

Factory Operations Visibility & Intelligence(1)

ANDOR

Software Design & Engineering Services(1)

ANDOR

Festo Didactic(1)

Connect?

Please feel encouraged to schedule a call with us:

Schedule a Call

Or directly send us an email:

team@iotone.com

1 case study

Case Study missing? Just let us know via Add New Case Study.

Compare

Deep Learning Boosts Robotic Picking Flexibility

Festo Didactic

Gripping and manipulating items of diverse shapes and sizes have long been one of the biggest challenges facing industrial robotics. The difficulty is perhaps best summed up by the Polanyi Paradox, which states that we "know more than we can tell." In essence, while it may be easy to teach machines to exhibit a high level of performance on tasks that require abstract reasoning such as running computations, it is substantially harder to grant them the sensory-motor skills of even a small child in all but the most standardized and predictable environments.However, with the need for flexible picking to accommodate reduced changeover time for more varied product runs on the rise, industry is pursuing new solutions to the problem.

Download PDF

X

selected