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Improvising Robots

These Resilient Robots Carry On Working After Suffering Damage

— June 1, 2015 — Tech
Researchers in France and the USA are developing a resilient robot that is capable of working even after receiving major damage. The environments in which robots work often make it difficult for maintenance to be carried out, which is why robots capable of carrying on after damage are set to become highly prized. Indeed this robot is inspired by animals in the wild that have to find a way to carry on and fend for themselves after suffering injury.

These robots make use of trial-and-error algorithms that enable them to adapt, find a way to compensate for their losses and continue to work. The entire process is conducted in a matter of minutes and doesn't require the conducting of lengthy self-diagnosis tests.

In other words, these resilient robots literally improvise on the fly and use programmed algorithms to mimic the responses and behavior of animals in the wild that have no choice but to find some way to survive.

Trend Themes
1. Resilient Robots - Developing robots that can continue working even after receiving major damage presents disruptive innovation opportunities in automation and robotics industries.
2. Trial-and-error Algorithms - Implementing trial-and-error algorithms in robotics opens doors for disruptive innovation in artificial intelligence and machine learning sectors.
3. Imitating Animal Behavior - Creating robots that mimic animal responses and behaviors to survive and adapt offers disruptive innovation opportunities in biomimicry and robotics fields.
Industry Implications
1. Automation - The development of resilient robots that can carry on working after suffering damage disrupts the automation industry by enabling increased reliability and reduced downtime.
2. Robotics - The implementation of trial-and-error algorithms in robotics disrupts the industry by enabling robots to adapt and compensate for damage without requiring lengthy self-diagnosis tests.
3. Artificial Intelligence - The utilization of trial-and-error algorithms in robotics presents disruptive innovation opportunities in the artificial intelligence industry, enhancing machine learning capabilities and adaptive behavior.
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