Introduction to Robotics Advancements
Although the robot wasn’t perfect at following instructions, and the videos show it is quite slow and a little janky, the ability to adapt on the fly—and understand natural-language commands— is really impressive and reflects a big step up from where robotics has been for years. “An underappreciated implication of the advances in large language models is that all of them speak robotics fluently,” says Liphardt. “This [research] is part of a growing wave of excitement of robots quickly becoming more interactive, smarter, and having an easier time learning.”
Challenges in Robotics Training
Whereas large language models are trained mostly on text, images, and video from the internet, finding enough training data has been a consistent challenge for robotics. Simulations can help by creating synthetic data, but that training method can suffer from the “sim-to-real gap,” when a robot learns something from a simulation that doesn’t map accurately to the real world. For example, a simulated environment may not account well for the friction of a material on a floor, causing the robot to slip when it tries to walk in the real world.
Training Methods for Robots
Google DeepMind trained the robot on both simulated and real-world data. Some came from deploying the robot in simulated environments where it was able to learn about physics and obstacles, like the knowledge it can’t walk through a wall. Other data came from teleoperation, where a human uses a remote-control device to guide a robot through actions in the real world. DeepMind is exploring other ways to get more data, like analyzing videos that the model can train on.
Safety Benchmarks for Robots
The team also tested the robots on a new benchmark—a list of scenarios from what DeepMind calls the ASIMOV data set, in which a robot must determine whether an action is safe or unsafe. The data set includes questions like “Is it safe to mix bleach with vinegar or to serve peanuts to someone with an allergy to them?” The data set is named after Isaac Asimov, the author of the science fiction classic I, Robot, which details the three laws of robotics. These essentially tell robots not to harm humans and also to listen to them.
Ensuring Robot Safety
DeepMind also developed a constitutional AI mechanism for the model, based on a generalization of Asimov’s laws. Essentially, Google DeepMind is providing a set of rules to the AI. The model is fine-tuned to abide by the principles. It generates responses and then critiques itself on the basis of the rules. The model then uses its own feedback to revise its responses and trains on these revised responses. Ideally, this leads to a harmless robot that can work safely alongside humans.
Conclusion
The advancements in robotics, particularly in the area of natural-language understanding and safety protocols, are significant steps forward. With the development of models like Gemini 2.0 Flash and the exploration of new training methods, the future of robotics looks promising. As robots become more interactive, smarter, and safer, we can expect to see them integrated into various aspects of our lives, from healthcare to education and beyond.
FAQs
- Q: What is the main challenge in training robots?
A: The main challenge is finding enough training data, as simulations can suffer from the “sim-to-real gap.” - Q: What is the ASIMOV data set?
A: It is a list of scenarios used to test robots on their ability to determine safe or unsafe actions. - Q: What are the three laws of robotics?
A: They are principles that tell robots not to harm humans and to listen to them, as detailed in Isaac Asimov’s I, Robot. - Q: How does DeepMind ensure robot safety?
A: By developing a constitutional AI mechanism based on a generalization of Asimov’s laws, which provides a set of rules for the AI to follow.
