Google DeepMind introduced a suite of new tools to enhance robot learning in unfamiliar environments, building on the RT-2 model and aiming for autonomous robots. AutoRT orchestrates robotic agents using large language and visual models, while SARA-RT improves efficiency using linear attention. RT-Trajectory introduces visual overlays for intuitive robot learning, resulting in improved success rates.
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Google DeepMind’s New Tools for Autonomous Robot Learning
AutoRT
AutoRT combines a Large Language Model (LLM) and a Visual Language Model (VLM) with a robot control model like RT-2. This allows robots to assess their environment, generate potential tasks, and evaluate them based on safety and capabilities.
Robotic Constitution
DeepMind has developed a set of guidelines, inspired by Isaac Asimov’s laws of robotics, to keep robots from selecting tasks that could cause harm or damage.
SARA-RT
SARA-RT makes models like RT-2 more efficient by using a linear attention model, resulting in a 14% speed improvement and 10% accuracy gains.
RT-Trajectory
RT-Trajectory adds a 2D visual overlay on a training video, allowing robots to learn tasks intuitively. This has led to significant success rate improvements compared to RT-2.
Practical AI Solutions for Middle Managers
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- Identify Automation Opportunities
- Define KPIs for AI Impact
- Select AI Solutions that Align with Your Needs
- Implement AI Gradually and Judiciously
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Google releases a suite of advanced robotic tools
- DailyAI
- Twitter – @itinaicom
