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Introducing AC one: Data collection efficiency increased by up to 10 times.🦾 Allows first-time users to get started in no time.🎆 Delivers impressive results on flexible objects.🧶 More details will be released in the coming weeks.
The world needs RL🦾
Our model can now learn from its own experience with RL! Our new π*0.6 model can more than double throughput over a base model trained without RL, and can perform real-world tasks: making espresso drinks, folding diverse laundry, and assembling boxes. More in the thread below.
Your bimanual manipulators might need a Robot Neck 🤖🦒 Introducing Vision in Action: Learning Active Perception from Human Demonstrations ViA learns task-specific, active perceptual strategies—such as searching, tracking, and focusing—directly from human demos, enabling robust…
Our models need to run in real time on real robots, but inference with big VLAs takes a long time. We developed Real-Time Action Chunking (RTC) to enable real-time inference with flow matching for the π0 and π0.5 VLAs! More in the thread👇
Introducing π-0.5! The model works out of the box in completely new environments. Here the robot cleans new kitchens & bedrooms. 🤖 Detailed paper + videos in more than 10 unseen rooms: physicalintelligence.company/blog/pi05 A short thread 🧵
Introducing Hi Robot – Hierarchical Interactive Robot Our first step at @physical_int towards teaching robots to listen and think harder. A 🧵 on how we make robots more steerable 👇
Arm🦾 ：ARX L5🤖👐
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