The article emphasizes that the dataset for RoboAgent is open source and universally accessible, designed to be used with readily available robotics hardware. This allows researchers and companies to utilize and build out a growing collection of robot data and skills. The goal is to create multipurpose robotics systems that can move beyond repetitive tasks in highly structured environments, paving the way for general-purpose robots. The article concludes by stating that while we are still at the beginning of these approaches to robotic learning, it is an exciting time for the development of emerging multipurpose systems.
Key takeaways:
- Robotic learning is advancing with systems like VRB (Vision-Robotics Bridge) from Carnegie Mellon University and RT-2 (Robotic Transformer 2) from Google's DeepMind, which can apply learnings from one environment to another and abstract away the minutia of performing a task, respectively.
- RoboAgent, a joint effort between CMU and Meta AI, combines active and passive learning, observing tasks performed via the internet and actively learning by remotely teleoperating the robot. This system can also apply learnings from one environment to another.
- The dataset used for RoboAgent is open source and universally accessible, and is designed to be used with readily available, off-the-shelf robotics hardware. This allows researchers and companies to utilize and build out a growing collection of robot data and skills.
- The goal of these advancements in robotic learning is to create multipurpose robotics systems that can eventually become general-purpose robots, moving beyond the repetitive machines in highly structured environments that are common in industrial settings.