Project Fetchとは？ロボット犬の操作で人間の約20倍速を記録

"In August 2025, we ran an experiment to see how much Claude could help Anthropic employees—who were not robotics experts—perform sophisticated (and amusing) tasks with an off-the-shelf robotic quadruped (henceforth, a robodog). We called this Project Fetch." — 冒頭の段落より

"Before we dragged our colleagues to a warehouse for the experiment, we double checked whether Opus 4.1 could do the tasks entirely on its own. Unquestionably, it could not. Much like our team without Claude, it got hung up on the preliminary task of figuring out how to connect to the robot." — 冒頭の段落より

"For this autonomous update, we couldn’t ask Claude to use a physical controller, nor did we evaluate the time it took a researcher to use the Claude-programmed controller to retrieve the ball (though we did confirm that it worked as intended). On the remaining subset of tasks, we ran three trials of Opus 4.7 using adaptive thinking with effort set to maximum in Claude Code." ／ "The role of our researcher was limited to plugging a laptop running Claude Code into the robodog, entering the initial prompt, approving commands, and approving the model to go to the next task." — What did we do?の節より

"Not only did they do that, but Claude Opus 4.7—operating without human assistance—was about 20 times faster than the fastest human team at all tasks completed by our participants less than a year ago." ／ "Very simply: on every task that was completed by at least one human team in August, Opus 4.7 completed the same task at least ten times faster." — 冒頭の段落とWhere did Claude excel?の節より

"If you consider the four tasks that were completed by both human teams, Opus 4.7 was, on average, more than 37 times faster than Team Claude-less and more than 18 times faster than Team Claude." ／ "Whereas the humans struggled to choose between multiple different approaches to interface with the dog’s sensors, Opus 4.7 was able to quickly identify the best path. Much of the code it wrote was effective on the first try (which was not the case for Team Claude or Team Claude-less in the original experiment)." ／ "The chart shows that Team Claude-less completed tasks in 361 minutes; Team Claude completed tasks in 181 minutes, and Claude Opus 4.7 alone completed tasks in 9 minutes 35 seconds. Opus 4.7 was 37.7 times faster than Team Claude-less and 18.9 times faster than Team Claude." ／ "Team Claude-less did not complete all 5 tasks in the table; Team Claude completed them in 264 minutes; and Opus 4.7, averaged over 3 trials, completed them in 12 minutes 7 seconds." — Where did Claude excel?の節と図表の代替テキストより

"Indeed, we can see evidence of Opus 4.7’s efficiency when we look at the volume of code it generated: it was as or more successful than both human teams while producing almost ten times less code than Team Claude." ／ "Team Claude wrote 10,309 lines of code; Team Claude-less wrote 1,136 lines of code; Opus 4.7 alone wrote 1,045 lines of code." — Where did Claude excel?の節とコード量の図の代替テキストより

"This required the ability to quickly perceive if the ball had gone off course, how that error related to the previous command, where the ball was now, and then how to adjust future inputs to more precisely move the ball. This is a kind of closed loop at which people excel (at least after making some mistakes and learning from them)." ／ "In our Phase Two experiments, Claude struggled to capture this subtlety." — Where did Claude struggle?の節より

"Like the humans who reached the phase of needing to write a program for autonomous beach ball retrieval, Claude was able to move the robot behind the ball and position it to knock the ball back to the starting point. But the efforts to do so were poorly controlled and (again, like our human participants) not successful." ／ "With more time and additional scaffolding, we think it is very likely that current generations of Claude could do the same." — Where did Claude struggle?の節より

"This doesn’t mean that LLMs have now solved robotics. Far from it. The latest Claude models still struggled with using the robot to precisely move the beach ball—the “fetching” part of Project Fetch. And none of the tasks in these experiments implicate the more challenging, low-level elements of robotic control, such as developing a specific actuation policy." — 冒頭の段落より

"However, once again, we are seeing a pattern whereby first, models are helpful to humans. Then, humans are helpful to models. Finally, models are largely able to do things themselves. We have seen this in cybersecurity and now the same dynamics are starting to take shape at the intersection of AI and the physical world." ／ "We report results from Claude Opus 4.7 because it was our most advanced non-Mythos-class model at the time we ran this experiment." — 冒頭の段落と脚注より

"It is worth underscoring (as we did in our previous post) that this progress is not the result of a concerted effort to improve the robotics capabilities of our models. These improvements, like so many others in the history of LLM development, have emerged from much more general scaling." ／ "Models building their own software tools might have seemed outlandish not long ago, but it is happening. It would be unwise to rule out the same trajectory in hardware." — Where did Claude excel?の節とWhat does this mean?の節より

"What is interesting and different is that we now seem much closer to a world where models will be able to use off-the-shelf physical tools with relative ease—at least for limited purposes. This is similar to how AI models used existing software editing tools like string-replace when they made the transition to more agentic coding." ／ "And for some tasks, a human in the loop controlling the robot may still outstrip the AI model with its (virtual) hand on the D-pad." — What does this mean?の節より