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Research
Introduction
Robotics is a rapidly growing field that has the potential to revolutionize the way we live and work. One of the key challenges in robotics is enabling robots to perform complex tasks that require dexterity, such as tying shoelaces or tightening a screw. In this article, we will explore two new AI systems that are helping to overcome this challenge.
Improving Imitation Learning with Two Robotic Arms
Until now, most advanced AI robots have only been able to pick up and place objects using a single arm. In our new paper, we present ALOHA Unleashed, which achieves a high level of dexterity in bi-arm manipulation. With this new method, our robot learned to tie a shoelace, hang a shirt, repair another robot, insert a gear and even clean a kitchen.
The ALOHA Unleashed method builds on our ALOHA 2 platform that was based on the original ALOHA (a low-cost open-source hardware system for bimanual teleoperation) from Stanford University.
ALOHA 2 is significantly more dexterous than prior systems because it has two hands that can be easily teleoperated for training and data collection purposes, and it allows robots to learn how to perform new tasks with fewer demonstrations.
We’ve also improved upon the robotic hardware’s ergonomics and enhanced the learning process in our latest system. First, we collected demonstration data by remotely operating the robot’s behavior, performing difficult tasks like tying shoelaces and hanging t-shirts. Next, we applied a diffusion method, predicting robot actions from random noise, similar to how our Imagen model generates images. This helps the robot learn from the data, so it can perform the same tasks on its own.
Example of a bi-arm robot straightening shoe laces and tying them into a bow.
Example of a bi-arm robot laying out a polo shirt on a table, putting it on a clothes hanger and then hanging it on a rack.
Example of a bi-arm robot repairing another robot.
Learning Robotic Behaviors from Few Simulated Demonstrations
Controlling a dexterous, robotic hand is a complex task, which becomes even more complex with every additional finger, joint and sensor. In another new paper, we present DemoStart, which uses a reinforcement learning algorithm to help robots acquire dexterous behaviors in simulation. These learned behaviors are especially useful for complex embodiments, like multi-fingered hands.
DemoStart first learns from easy states, and over time, starts learning from more difficult states until it masters a task to the best of its ability. It requires 100x fewer simulated demonstrations to learn how to solve a task in simulation than what’s usually needed when learning from real world examples for the same purpose.
The robot achieved a success rate of over 98% on a number of different tasks in simulation, including reorienting cubes with a certain color showing, tightening a nut and bolt, and tidying up tools. In the real-world setup, it achieved a 97% success rate on cube reorientation and lifting, and 64% at a plug-socket insertion task that required high-finger coordination and precision.
Example of a robotic arm learning to successfully insert a yellow gear in simulation (left) and in a real-world setup (right).
Example of a robotic arm learning to tighten a bolt on a screw in simulation.
The Future of Robot Dexterity
Robotics is a unique area of AI research that shows how well our approaches work in the real world. For example, a large language model could tell you how to tighten a bolt or tie your shoes, but even if it was embodied in a robot, it wouldn’t be able to perform those tasks itself.
One day, AI robots will help people with all kinds of tasks at home, in the workplace and more. Dexterity research, including the efficient and general learning approaches we’ve described today, will help make that future possible.
We still have a long way to go before robots can grasp and handle objects with the ease and precision of people, but we’re making significant progress, and each groundbreaking innovation is another step in the right direction.
Frequently Asked Questions
Q1: What is ALOHA Unleashed?
ALOHA Unleashed is a new AI system that helps robots learn to perform complex tasks that require dexterity, such as tying shoelaces or tightening a screw.
Q2: What is DemoStart?
DemoStart is a reinforcement learning algorithm that helps robots acquire dexterous behaviors in simulation, which can then be transferred to real-world performance.
Q3: How does ALOHA Unleashed work?
ALOHA Unleashed uses a diffusion method to predict robot actions from random noise, similar to how our Imagen model generates images. This helps the robot learn from the data, so it can perform the same tasks on its own.
Q4: What are the applications of ALOHA Unleashed and DemoStart?
ALOHA Unleashed and DemoStart have the potential to revolutionize the way we live and work by enabling robots to perform complex tasks that require dexterity, such as helping people with daily tasks or assisting in industrial settings.
Q5: What is the future of robot dexterity?
The future of robot dexterity is promising, with significant progress being made in the development of AI systems that can enable robots to perform complex tasks with ease and precision. However, there is still much work to be done before robots can truly mimic human dexterity.
Conclusion
In conclusion, ALOHA Unleashed and DemoStart are two new AI systems that are helping to overcome the challenge of enabling robots to perform complex tasks that require dexterity. These systems have the potential to revolutionize the way we live and work, and we are excited to see the impact they will have in the future.
