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Introducing 2️⃣ new AI systems for robotics: 🤖 ALOHA Unleashed to perform two-armed manipulation tasks 🦾 DemoStart to control a multi-fingered robotic hand They learned to tackle a range of actions requiring dexterity. Here's how. 🧵

297,118 görüntüleme • 1 yıl önce •via X (Twitter)

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Google DeepMind profil fotoğrafı
Google DeepMind1 yıl önce

Manipulating objects using just one robot arm is challenging. Enter ALOHA Unleashed, which builds upon our ALOHA 2 system. With two arms, it can be teleoperated to collect high quality training data. With this system, robots can perform new tasks with fewer demonstrations.

Google DeepMind profil fotoğrafı
Google DeepMind1 yıl önce

We improved the hardware’s ergonomics while combining ALOHA 2 with a diffusion method - which generates realistic actions from random noise - to teach complex skills. This enabled our bi-arm system to push the boundaries of learning to accomplish difficult tasks autonomously.

Google DeepMind profil fotoğrafı
Google DeepMind1 yıl önce

To challenge our robot, we set out to see if it could perform in both simulation and the real-world. Our results show it was able to: ▪️Tie shoelaces ▪️Insert gears ▪️Replace parts on another robot ▪️Hang up shirts ▪️Clean up a kitchen

Google DeepMind profil fotoğrafı
Google DeepMind1 yıl önce

An AI controlling a multi-fingered robotic hand means it could carry out more useful, physical actions. DemoStart takes us one step closer by using a reinforcement learning algorithm to master various behaviors from just a handful of simulated demonstrations.

Google DeepMind profil fotoğrafı
Google DeepMind1 yıl önce

Over time, DemoStart considers increasingly difficult states in simulation until it masters a task. This staged learning approach enables it to successfully transfer knowledge to the real world - reducing the cost and time needed for running physical experiments.

Google DeepMind profil fotoğrafı
Google DeepMind1 yıl önce

We tested it on the DEX-EE hand, developed with @shadowrobot. In simulation, the robot had to reorient a 3D cube to show a certain color. It then performed this task in the real world 97% of the time. It was also able to reliably insert a plug into a socket in both set-ups.

Google DeepMind profil fotoğrafı
Google DeepMind1 yıl önce

There’s still a long way to go for robotics to achieve human-level dexterity but the future is exciting. Our research could pave the way in creating more helpful, dexterous robots that could one day assist in the home, office and beyond. Find out more →

Kayla Cardillo profil fotoğrafı
Kayla Cardillo1 yıl önce

Shouldn’t robots have their own way of doing things? Making the robots copy humans just looks slow

Architect🛡️ profil fotoğrafı
Architect🛡️1 yıl önce

i also hang my shirts this way superintelligence 🤝

kache profil fotoğrafı
kache1 yıl önce

thank you for sharing!!

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