Can robots learn from machine dreams?
MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI’s potential for creating robotics training data.
Robotics
A faster, better way to train general-purpose robots
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
Read MoreCombining next-token prediction and video diffusion in computer vision and robotics
A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.
Read More
The Safety and Ethics of Tesla’s Robot Revolution
Tesla’s recent innovations in autonomous vehicles and robots present a transformative vision for transportation and work. However, concerns over safety, regulatory oversight, job displacement, and data privacy highlight significant risks. As Tesla advances, the need for effective regulations to protect public interests and ensure responsible use of technology becomes critical.
Read MoreHow AI is improving simulations with smarter sampling techniques
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
Read MoreHelping robots zero in on the objects that matter
A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.
Read More
The Great AI Robot Showdown: Figure F.02 vs. R2-D2 (with a Dash of C-3PO)
In this article, Figure’s F.02 humanoid robot is compared to iconic Star Wars droids R2-D2 and C-3PO. While F.02 excels in modern design and advanced technology, R2-D2 and C-3PO win in timeless appeal, versatility, personality, and historical influence. The article also emphasizes the increasing real-world applications of robots in various industries, shaping our present and future.
Read MoreHelping robots practice skills independently to adapt to unfamiliar environments
A new algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.
Read MorePrecision home robots learn with real-to-sim-to-real
CSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.
Read MoreCreating and verifying stable AI-controlled systems in a rigorous and flexible way
Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.
Read More
You must be logged in to post a comment.