Anthropic has demonstrated how AI controls a robot dog faster and more efficiently than a human.
Anthropic has demonstrated an impressive leap in artificial intelligence autonomy as part of its updated Project Fetch experiment. The Claude Opus 4.7 model handled the setup and control of a robot dog 20 times faster than teams of human engineers working with previous AI versions.
Back in August 2024, employees with no robotics experience attempted to program a four-legged robot, and at that time, the neural network only accelerated the search for solutions. Now, Claude Opus 4.7 acted almost entirely autonomously under minimal researcher supervision. The language model independently connected to video sensors and LiDAR, wrote a manual control program, created a robot path monitoring system, and configured an object recognition algorithm.
Test results showed that Opus 4.7 was 18 times faster than a team using older AI versions and 37 times faster than humans without chatbot assistance. Additionally, the neural network generated code that was 10 times smaller in volume than that produced by human teams. This indicates significantly higher efficiency and conciseness in the solutions proposed by the AI.
The experiment's authors emphasize that progress in robotics has been a side effect of the general scaling of language models. Anthropic did not implement specialized algorithms for controlling hardware — this is all a result of the evolution of basic architectures.
However, there were limitations. Claude still struggles with precise physical actions. The model managed to guide the robot to its target but failed to gently push a ball to the exact desired spot. Such operations require complex real-time feedback, where humans still hold the advantage.
Anthropic believes the industry is entering an era of "physical AI agents." In the future, neural networks will be able to use standard tools and equipment as effectively as they currently work with software code.
Expert opinion: This experiment is a clear signal that the boundaries between the digital and physical worlds are blurring faster than many expect. If AI is already capable of autonomously controlling robots, we will see mass adoption of such systems in logistics, manufacturing, and even household robotics in the coming years. However, the problem of precise physical feedback remains the "Achilles' heel" — for now, humans literally keep their fingers on the pulse.