Media Archive

Researchers at NVIDIA recently developed a controller that could help expand the motion repertoire of humanoid robots.

As building bigger and better chatbots gets harder, AI researchers turn to systems that learn to simulate the world.

On platforms like Hugging Face, AI models for robotics gain traction.

NVIDIA Isaac GR00T N1 is the world’s first open foundation model for generalized humanoid robot reasoning and skills.

RoboCasa is a large-scale simulation framework that can be used to train generalist robots to complete various tasks in everyday settings.

Recent months have seen a growing number of projects that use large language models (LLMs) to create robotics applications that seemed impossible before.

AI agent uses LLMs to automatically generate reward algorithms to train robots to accomplish complex tasks.

The video aims to demonstrate DRACO 3's ability to prepare a meal using virtual reality (VR) teleoperation, a technical term used in robotics that describes a human operator controlling a robot remotely.

AI researchers have built a Minecraft bot that can explore and expand its capabilities in the game's open world — but unlike other bots, this one basically wrote its own code through trial and error and lots of GPT-4 queries.

A variety of new tests seeks to assess AI's ability to reason and learn concepts.

AI researchers say they’ve created a framework for controlling four-legged robots that promises better energy efficiency and adaptability than more traditional model-based gait control of robotic legs.

In the future, RoboTurk could become a key resource in the field of robotics, aiding the development of more advanced and better performing robots.

With a smartphone and a browser, people worldwide will be able to interact with a robot to speed the process of teaching robots how to do basic tasks.

Robots learning to do things by watching how humans do it? That’s the future.

Robots are getting better at dealing with the complexity of the real world, but they still need a helping hand when taking their first tentative steps outside of easily defined lab conditions.

A new digital training ground that replicates an average home lets AI learn how to do simple chores like slicing apples, making beds, or carrying drinks in a low-stakes environment.

AI2-THOR, an interactive simulation based on home environments, can prepare AI for real-world challenges.

A new database will gauge progress in artificial intelligence, as computers try to grasp what’s going on in scenes shown in photographs.