The AI model Claude Opus 4.7 has surpassed humans in controlling a robot dog: speed is 20 times higher
As part of the updated Project Fetch experiment, Anthropic has demonstrated an impressive breakthrough in integrating artificial intelligence with physical systems. My team analyzed the results: the Claude Opus 4.7 model completed tasks for configuring and controlling a robotic dog 20 times faster than the best human engineering teams.
Autonomous operation without human intervention
While in August 2024, AI only assisted employees without robotics experience, helping them find solutions faster, the situation has now changed dramatically. In the new testing phase, Claude Opus 4.7 operated almost completely autonomously, under minimal researcher supervision. The neural network independently performed a full cycle of operations:
- connected to video sensors and lidar;
- wrote a program for manual robot control;
- created a trajectory monitoring system;
- configured an object recognition algorithm.
Numbers that speak for themselves
The model's performance was not just high—it was revolutionary. According to my data, Claude Opus 4.7 proved to be 18 times faster than a team using older AI versions, and 37 times faster than humans working without chatbot assistance. Additionally, the neural network generated code that was 10 times smaller in volume than that of human teams, indicating much higher efficiency and optimization.
Limitations and prospects
It is important to note that the progress in robotics has essentially become a side effect of the general scaling of language models. Anthropic did not implement specialized algorithms for controlling hardware—this is a pure achievement of the basic AI architecture.
However, there were caveats. Claude still struggles with precise physical actions. The model successfully guided the robot to the target but could not neatly push a ball to the exact spot—a task requiring complex real-time feedback, where humans still maintain superiority.
Looking to the future
Anthropic is confident that the industry is entering an era of "physical AI agents." In the coming years, neural networks will learn to use standard tools and equipment as effectively as they currently work with software code. This opens up enormous opportunities for automating manufacturing, logistics, and other industries.
Expert commentary: As a leading analyst, I believe this experiment is an important signal for the entire industry. We are witnessing not just an improvement in algorithms, but a paradigm shift: AI is ceasing to be only a virtual assistant and is becoming a full-fledged agent in the physical world. Investors should closely monitor the development of this direction—it could become the next major driver of market growth.