The AI model Claude Opus 4.7 has surpassed human engineers in controlling a robot dog — task execution speed increased by 20 times
Anthropic has unveiled updated results from the Project Fetch experiment, and they are impressive. The new version of the Claude Opus 4.7 language model demonstrated the ability to autonomously configure and control a four-legged robot, completing tasks 20 times faster than teams of human engineers.
As a reminder, in August 2024, Anthropic employees with no robotics experience attempted to program a robot dog, and at that time, the AI only helped them find solutions faster. However, in the new testing phase, Claude Opus 4.7 operated almost entirely autonomously, under minimal researcher supervision. The neural network independently:
- connected to video sensors and LiDAR;
- wrote a program for manual control;
- created a system for monitoring the robot's path;
- configured an object recognition algorithm.
The numbers speak for themselves: the Opus 4.7 model was 18 times faster than a team using older AI versions and 37 times faster than humans who did not use chatbot assistance. Moreover, the neural network wrote more efficient code—its volume turned out to be 10 times smaller than that of human teams.
The authors emphasize that progress in robotics is a side effect of the general scaling of language models. Anthropic did not introduce specialized algorithms for controlling hardware—this is all the result of the evolution of basic architectures.
However, not everything is smooth. Despite the success, Claude still struggles with precise physical actions. The model managed to guide the robot to the target but failed at the task of gently pushing a ball to a specific point. This requires complex real-time feedback, in which humans still outperform AI.
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.
My analysis: This experiment is a clear signal that the boundary between the digital and physical worlds is blurring. If earlier AI was merely an assistant in coding, it is now becoming a full-fledged operator of complex equipment. However, the problem with precise motor skills reminds us that full autonomy in the real world is still far off. Investors should pay attention to companies integrating AI into robotics—this could become the next big trend.