Case Studies.

Add Case Study

Our Case Study database tracks 1,841 solution providers in the global IoT Ecosystem.
Filters allow you to explore case studies quickly and efficiently.

Filters
  • (1)
    • (1)
  • (1)
  • (1)
  • (1)
  • (1)
  • (1)
Selected Filters
1 case study
Sort by:
Deep Learning Boosts Robotic Picking Flexibility - Festo Didactic Industrial IoT Case Study
Deep Learning Boosts Robotic Picking Flexibility
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

Contact us

Let's talk!
* Required
* Required
* Required
* Invalid email address
By submitting this form, you agree that IoT ONE may contact you with insights and marketing messaging.
No thanks, I don't want to receive any marketing emails from IoT ONE.
Submit

Thank you for your message!
We will contact you soon.