Categories: FAANG

RoboCat: A self-improving robotic agent

Robots are quickly becoming part of our everyday lives, but they’re often only programmed to perform specific tasks well. While harnessing recent advances in AI could lead to robots that could help in many more ways, progress in building general-purpose robots is slower in part because of the time needed to collect real-world training data. Our latest paper introduces a self-improving AI agent for robotics, RoboCat, that learns to perform a variety of tasks across different arms, and then self-generates new training data to improve its technique.

Eureka! NVIDIA Research Breakthrough Puts New Spin on Robot Learning

A new AI agent developed by NVIDIA Research that can teach robots complex skills has trained a robotic hand to perform rapid pen-spinning tricks — for the first time as well as a human can. The stunning prestidigitation, showcased in the video above, is one of nearly 30 tasks that…

October 21, 2023

In "FAANG"

Needle-Moving AI Research Trains Surgical Robots in Simulation

A collaboration between NVIDIA and academic researchers is prepping robots for surgery. ORBIT-Surgical — developed by researchers from the University of Toronto, UC Berkeley, ETH Zurich, Georgia Tech and NVIDIA — is a simulation framework to train robots that could augment the skills of surgical teams while reducing surgeons’ cognitive…

May 16, 2024

In "FAANG"

AI training method helps robots carry lab-learned skills into real-world tasks

Robots are trained for specific tasks, such as cutting, using simulation. However, collecting real-world data is expensive, slow, and sometimes unsafe, particularly for tasks involving physical interaction. A new AI-based method co-developed by Aston University's Dr. Alireza Rastegarpanah could revolutionize the way advanced robotic systems are trained for real-life tasks,…

May 7, 2026

In "AI/ML News"

AI Generated Robotic Content