Google Deepmind has developed AutoRT, utilizing foundation models to enable the autonomous deployment of robots in diverse environments with minimal human supervision. It leverages vision-language and large language models to generate task instructions and ensure safety through a robot constitution framework. AutoRT facilitates large-scale robotic data collection and enhances robotic learning and autonomy in real-world scenarios.
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AutoRT: Transforming Robotic Learning Through AI-Driven Task Execution in Real-World Environments
Google Deepmind has introduced AutoRT, a system that uses existing foundation models to improve the deployment of operational robots in new scenarios with minimal human supervision. This addresses the challenge of training robots in real-world situations with limited data. AutoRT leverages vision-language models for scene understanding and grounding, and large language models for generating diverse instructions for a fleet of robots, enabling them to adapt to new environments and tasks autonomously.
Key Components of AutoRT
The system begins with exploration, where robots navigate and map the environment using a natural language map approach. It also involves a robot constitution inspired by Asimov’s laws, which sets foundational, safety, and embodiment rules for safe and effective task generation. Task generation incorporates scene description by vision-language models and task proposal by large language models, with specific prompts for each robot’s collect policy. Affordance filtering ensures the feasibility and safety of generated tasks. AutoRT employs diverse collection policies, including teleoperation, scripted pick policies, and autonomous policies, aiming to maximize data diversity. Guardrails enhance safety in real-world settings.
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