Abstract: The inherent compliant nature of soft robots can offer remarkable advantages over their rigid counterparts in terms of safety to human users and adaptability in unstructured environments.

Current continual learning methods can utilize labeled data to alleviate catastrophic forgetting effectively. However, ...

Google researchers introduce ‘Internal RL,’ a technique that steers an models' hidden activations to solve long-horizon tasks ...

Robots are learning from videos to execute tasks, thanks to a new AI model ...

An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...

LAMDA-SSL toolkit delivers the first unified benchmarks and robust algorithms that safely exploit unlabeled data despite ...

Most robot headlines follow a familiar script: a machine masters one narrow trick in a controlled lab, then comes the bold promise that everything is about to change. I usually tune those stories out.

What is catastrophic forgetting in foundation models? Foundation models excel in diverse domains but are largely static once deployed. Fine-tuning on new tasks often introduces catastrophic forgetting ...

According to @AIatMeta, DINOv3 leverages self-supervised learning (SSL) to train on 1.7 billion images using a 7-billion-parameter model without the need for labeled data, which is especially ...

Abstract: This article conducts a large dimensional study of a simple yet quite versatile classification model, encompassing at once multi-task and semi-supervised learning, and taking into account ...