Slithering to Autonomy: A Snake Robot Navigates the Real World

Researchers have developed a complete navigation system enabling a snake robot to autonomously follow waypoints and adapt to complex terrains.

Researchers have developed a complete navigation system enabling a snake robot to autonomously follow waypoints and adapt to complex terrains.

New research reveals that while artificial intelligence systems can flawlessly solve formal logic problems, they struggle with reasoning based on real-world knowledge and are surprisingly susceptible to human biases.

A new framework, SAGA, empowers robots to perform complex manipulation tasks in real-world environments by grounding semantic understanding in structured affordances.
A new approach leverages artificial intelligence to overcome development hurdles and breathe life back into critical, yet neglected, scientific software projects.
![Evaluations demonstrate the trained model’s performance using scene reconstruction on real-world data gathered from a 7-degree-of-freedom WAM robot arm, as detailed in Sousa et al.’s work [sousa2014].](https://arxiv.org/html/2512.11900v1/x4.png)
New research demonstrates that symbolic regression and sparse identification techniques can unlock clear, physically-meaningful insights into how robots move, offering a powerful alternative to complex neural networks.

Researchers now have a new framework to navigate the complex landscape of artificial intelligence models for software engineering tasks.

A new architecture, CoRA, dramatically improves the efficiency and robustness of vehicle-to-everything (V2X) perception systems by intelligently decoupling performance from communication overhead.
As AI agents become increasingly sophisticated, their ability to learn and adapt hinges on robust and well-designed memory systems.

A new deep learning framework is improving the accuracy of emotion recognition in autistic children during interactions with social robots, paving the way for more effective support and diagnosis.
A new open-access platform combines the power of artificial intelligence with materials science databases to automate research and accelerate discovery.