Categories: AI/ML News

Ant insights lead to robot navigation breakthrough

Have you ever wondered how insects are able to go so far beyond their home and still find their way? The answer to this question is not only relevant to biology but also to making the AI for tiny, autonomous robots. Drone-researchers felt inspired by biological findings on how ants visually recognize their environment and combine it with counting their steps in order to get safely back home. They have used these insights to create an insect-inspired autonomous navigation strategy for tiny, lightweight robots. It allows such robots to come back home after long trajectories, while requiring extremely little computation and memory (0.65 kiloByte per 100 m). In the future, tiny autonomous robots could find a wide range of uses, from monitoring stock in warehouses to finding gas leaks in industrial sites.
AI Generated Robotic Content

Share
Published by
AI Generated Robotic Content

Recent Posts

Update: Distilled v1.1 is live

We've pushed an LTX-2.3 update today. The Distilled model has been retrained (now v1.1) with…

12 hours ago

How to Implement Tool Calling with Gemma 4 and Python

The open-weights model ecosystem shifted recently with the release of the

12 hours ago

Structured Outputs vs. Function Calling: Which Should Your Agent Use?

Language models (LMs), at their core, are text-in and text-out systems.

12 hours ago

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts

This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation…

12 hours ago

How to build effective reward functions with AWS Lambda for Amazon Nova model customization

Building effective reward functions can help you customize Amazon Nova models to your specific needs,…

12 hours ago

How to find the sweet spot between cost and performance

At Google Cloud, we often see customers asking themselves: "How can we manage our generative…

12 hours ago