Nvidia, Alphabet team on grasping robotics
Intrinsic, a software and AI robotics company at Alphabet, has integrated Nvidia’s AI and Isaac platform technologies for autonomous robotic manipulation.
Intrinsic is using the foundation models from Nvidia Isaac Manipulator for autonomous grasping for industrial automation, creating a prototype for Trumpf Machine Tools.
Grasping has been a long sought after robotics skill. So far it’s been time-consuming, expensive to program and difficult to scale. As a result, many repetitive pick-and-place conditions haven’t been seamlessly handled to date by robots.
Simulation, AI and synthetic data is changing that. Intrinsic used Isaac Sim on the Omniverse platform to generate synthetic data for vacuum grasping using computer-aided design models of sheet metal and suction grippers.
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This was fed into Isaac Manipulator, which is a collection of foundation models and modular GPU-accelerated libraries that help industrial automation companies build scalable and repeatable workflows for dynamic manipulation tasks by accelerating AI model training and task reprogramming.
Foundation models are based on a transformer deep learning architecture that allows a neural network to learn by tracking relationships in data. This enables robot perception and decision-making like never before and provides zero-shot learning with the ability to perform tasks without prior examples.
The prototype uses Intrinsic Flowstate, a developer environment for AI-based robotics solutions, for visualizing processes, associated perception and motion planning. With a workflow that includes Isaac Manipulator, one can generate grasp poses and CUDA-accelerated robot motions, which can first be evaluated in simulation with Isaac Sim — a cost-saving step — before deployment in the real world with the Intrinsic platform.
Under the collaboration, NVIDIA and Intrinsic plan to bring state-of-the-art dexterity and modular AI capabilities for robotic arms, with a robust collection of foundation models and GPU-accelerated libraries to accelerate a greater number of new robotics and automation tasks.
“For the broader industry, our work with NVIDIA shows how foundation models can have a profound impact, including making today’s processing challenges easier to manage at scale, creating previously infeasible applications, reducing development costs, and increasing flexibility for end users,” said Wendy Tan White, CEO at Intrinsic.
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