Spot uses dynamic whole-body manipulation to autonomously upright, roll, drag, and stack 15kg car tires using an approach that combines reinforcement learning and sampling-based optimization
We're creating future generations of robots.
Robotics has come a long way in the past 40 years, but we still have a long way to go.
We need to make robots smarter, more agile and dexterous, and generally easier to use - more like people. Once we do that, robots and other types of intelligent systems will increase productivity, free people from dangerous work, care for the disabled, and help people live better lives.
Marc Raibert
Executive Director, Robotics and AI Institute
We believe the development of future generations of intelligent machines requires four core areas of research.
Data-Driven Large AI Models
Developing foundation models to enable robots to perform complex physical tasks, including tight tolerance assembly and force-mediated behaviors.
Dexterous Manipulation
Developing methods and hardware to enable robots to dynamically manipulate physical objects and systems for useful tasks like assembly, repair, and transport.
Advanced Learning for Control
Enabling robots to safely interact with each other and the physical world around them to perform natural and robust dynamic behavior.
Navigation in Challenging Environments
Building mobility capabilities that allow robots to navigate diverse environments, including indoor spaces and challenging terrain, by understanding their surroundings.
Ethics of Robots in Society
Exploring how the impact of robot integration in daily life – including workplaces, homes, and other shared spaces – affects human attitudes and actions.
Let’s change the way the world thinks of robots.
Join us in creating the world’s premier research institute focused on intelligent machines.
Featured Resources
Designing Wheeled Robotic Systems for Ultra Mobility and Performance
Unlocking physical capabilities with artificial intelligence to build the next generation of machines.
Diffuse-CLoC: Bridging Physics and Control in Character Animation
Researchers at the RAI Institute have bridged the gap between kinematic motion diffusion models and physics-based control policies with a new guided diffusion framework called Diffuse-CLoC.