再次与我们的合作伙伴 Andon Labs：https://andonlabs.com/ 合作，我们开发了一系列新的演示和评估，评估 AI 模型使用飞行无人机自主执行简单的定位与跟随任务的能力，这类任务通常用于空中监控，最终形成了一个新的基准测试：Drone-Bench。
我们预计 AI 模型将在许多人类能做到的事情上变得普遍有能力。操作硬件，特别是机器人，就是这样的一种能力。能够做到这一点，为 AI 在经济中发挥作用打开了广阔的空间，但同样也带来了新的风险领域。Anthropic 设立前沿红队（Frontier Red Team）：https://www.anthropic.com/research/team/frontier-red-team 的一个关键原因就是为了测量这类能力，从而让我们对 AI 能否自主操作机器人这一世界有清晰的情境认知——以及随之而来的所有利弊。空中无人机尤其重要，因为它们易于获取，且职业人士和爱好者经常使用。它们已被用于提高农业产量和在战争中锁定敌方目标。像 AI 本身一样，无人机也是一项双用途技术；因此，获得关于它们交叉应用的更好证据至关重要。
值得强调的是，该评估的基线既不是无人协助的人类能力下限，也不是通过人类与 AI 合作可以达到的上限。而是表明在使用现实的现代工具的情况下，AI 专家（但不是全职机器人专家）目前可以实现的水平。一个有趣的问题是，当模型基本自主运行时，是否以及何时能够可靠地通过这一合理且现实的努力基线，因为这正是减少人类监督压力可能加大的时刻——从而使得对适当人类角色的审慎、针对特定用例的判断尤为重要。
Several of our research projects over the last year have looked at how frontier models interact with the physical world. In Project：https://www.anthropic.com/research/project-vend-1 Vend：https://www.anthropic.com/research/project-vend-2, AI models ran a small shop; Project Fetch：https://www.anthropic.com/research/project-fetch-robot-dog was an early look at robots as the intermediary between digital models and physical objects. As we recently noted in Project Fetch: Phase two：https://www.anthropic.com/research/project-fetch-phase-two, we’re already seeing improvements in model capability such that their ability to use off-the-shelf robots is on track to approach the ease with which coding agents use software tools.
Working again with our partners at Andon Labs：https://andonlabs.com/, we developed a new series of demonstrations and evaluations that assess AI models’ ability to use a flying drone to autonomously perform a simple locate-and-follow task of the kind used in aerial surveillance, culminating in a new benchmark: Drone-Bench.
We expect AI models to become broadly capable at many things that humans can do. Operating hardware, in particular robots, is one such capability. Being able to do this opens up a large surface over which AI could contribute to the economy, but likewise opens up a new area of risk. A key reason why Anthropic has a Frontier Red Team：https://www.anthropic.com/research/team/frontier-red-team is to measure capabilities like this, giving us situational awareness into how close we are to the world in which AI can autonomously pilot robots—with all the attendant benefits and risks. Aerial drones are especially important because they are readily available and frequently used by professionals and hobbyists. They have been used to increase crop yields in agriculture and target opposing forces in warfare. Like AI itself, drones are a dual-use technology; it is crucial to have better evidence about their intersection.
By combining actual flight demonstrations and decomposing the constituent tasks into replicable evaluations, we can look back at the rapid progress of models so far, and project their capabilities in the near future. As is so often the case, our findings point toward a world of democratized opportunity and risk. Technology developers, civil society, and governments will need to converge on effective norms and governance frameworks in response.
The core task we tested in Project Fetch—getting a robot dog to retrieve a beach ball—was neither especially practical nor especially concerning. In this project, we chose an objective with clearer utility and policy relevance: a simple locate-and-follow task used in aerial surveillance. Capabilities like automated person-detection and tracking can have legitimate purposes such as search and rescue, disaster response, and lawful public safety uses. But this is a class of capabilities that is also subject to abuse, either through overreach of a legitimate authority or by unaccountable private individuals or organizations. The work we report here thus more closely matches the “dual-use” nature of AI models.
In these experiments, we ask the model to control a quad-rotor drone in an indoor office environment in order to locate and follow a person. 1 This requires a number of complex sub-tasks. The AI model needs to develop schema for controlling the aircraft, mapping and navigating the obstacle-laden indoor space, finding the target individual from a reference photo, and following them (plus reacquiring the target if they move out of frame).
Individually, there are known algorithms for accomplishing all of these tasks. What is not trivial is for the AI model to understand the challenges, identify the preexisting resources it can use to solve them, adapt those off-the-shelf solutions to its current situation, and execute the mission in real time. As we will see, the difficulty—both individually and in chaining these tasks together—is sufficient to distinguish between models of varying intelligence and plot the trajectory of capability improvement.
Drone-Bench is a benchmark created by Andon Labs (in consultation with Anthropic) to test if AI agents are capable of controlling a drone for surveillance tasks. Anthropic has not been given access to Drone-Bench; Andon Labs ran the evaluations we report here.
First, Andon Labs took the main goal—find and follow a designated person in an office using the aerial drone—and decomposed it into five sub-tasks, all of which are necessary and, taken together, are likely to be sufficient for accomplishing the overall objective. These sub-tasks are:
Next, each of these real-world tasks was reproduced in software so that we could run the models through them multiple times and far faster than needing to set up the physical demo for each instance (this is an improvement over Project Fetch, for example, which was an entirely physical experiment).
It was also important to establish a meaningful baseline of performance. Human-only baselines increasingly don’t reflect the reality of contemporary software engineering, so Andon worked with coding agents to develop algorithms for each sub-task. Putting all of these algorithms crafted by human-AI teams together allowed them to demonstrate end-to-end success, as shown in the below video.
A task is considered completed if the model meets or exceeds the baseline. Thus, if a model can complete all tasks, we can infer that it has the ability to autonomously control a drone to do at least as well on this surveillance task as the team at Andon Labs did.
For more details, check out Andon Labs’ post：https://andonlabs.com/evals/drone-bench about Drone-Bench.
It is worth underscoring that the evaluation’s baseline is neither the floor of unassisted human capability nor the ceiling of what is possible with concerted human-AI collaboration. Rather, it is indicative of what can be achieved in the present by AI experts (but not full-time roboticists) using a realistic suite of modern tools. The interesting question is if and when models operating essentially autonomously reliably pass this baseline of reasonable and realistic effort, as that is the point at which pressure to reduce human oversight may intensify—making deliberate, use case-specific judgments about the appropriate human role all the more important.
Andon tested 15 models from three developers: GPT-4o, GPT-4o Nov, o1, o3, Claude Opus 4, Gemini 2.5 Pro, GPT-5, Gemini 3.1 Pro, Opus 4.5, GPT-5.2, Opus 4.7, GPT-5.5, Opus 4.8, Fable 5, and GPT-5.6 Sol. The overall trend we observe is that newer models get successively further on all sub-tasks. Of these tasks, models are most successful at detection and following, and least successful at reconstruction and localization.
The best performing model was Claude Fable 5, which brings the frontier past the baseline on all tasks except reconstruction. When we then tested its ability to execute the entire demonstration end-to-end on the real drone, it performed noticeably better than the baseline at detecting and following.
However, due to errors from reconstruction that compounded in localization and navigation, it was unable to autonomously navigate between rooms (as you can see in the first part of the below video).
Clearly, Fable’s failure to accurately reconstruct the room is a huge stumbling block. But given models’ capabilities in the other phases, it really just amounts to the missing piece. Once it’s in place, end-to-end performance will suddenly be within reach. This is an advantage of decomposing the evaluation into constituent tasks: we are better positioned to avoid surprise. What would look like a discontinuous jump is revealed to be gradual progress in several necessary, but not sufficient, sub-tasks.
The sub-task view also surfaces encouraging signs. A trend we're seeing when reading Fable 5's submissions is that the model is doing local analysis before submitting its implementation. In one submission, the model calculated the drone's camera extrinsics by analyzing a video from the simulation, estimating the camera tilt to within four degrees of the true value by using the grout lines on the floor to recover the scene's vanishing point. You can see its process below:
In another run, Fable 5 built a 2D top-down reconstruction of what it thought the Follow task's environment looked like, so it could test and iterate on its implementation locally before burning a submission.
The environment differs from the real environment (seen below), but it helped Fable catch some easy bugs!
It’s important to understand how consistently models reach the reference level of performance, as well as whether they can reach it. Here there is obvious room for improvement. When we run 10 simulations, the models reach the human baseline in at least one simulation for four of five tasks. But even Fable 5, the current frontier model, reaches the human baseline on average for only three of the five tasks—and that level of consistency followed six months after the human baseline was exceeded as a one-off for the first time.
Although the complexity of this experiment is greater than some of our previous work and the operating environment of an actual (or simulated) office is more challenging than a wide-open warehouse, the experiment has important limitations: the drones are moving at slow speeds, we only tested in one office floorplan with a limited number of people, and Andon did not test outdoors in large crowds, among many other factors that would have made this more realistic. We still think this pilot provides a real signal about the direction of model capabilities: this evaluation will provide meaningful information about the underlying performance and reliability of models for autonomous targeting and tracking, even though more realistic and diverse experiments would be needed to assess operational capability.
This experiment highlights the potential of commercial-off-the-shelf (COTS) hardware and AI-tailored software to support useful, but possibly risky, tasks.
It is important to take seriously the parallel between AI models’ use of software in agentic coding and AI models’ control of hardware. In the early days of agentic coding, humans approved nearly every tool call. But after only a few months, models are now much more trusted to execute long-horizon tasks with minimal intervention.
In project Fetch, we examined how humans can use models to get robots to perform complex tasks. Now, we investigate many models on a large variety of different robotics tasks in simulation, to see how good models are at controlling robots themselves.
Get updates on our latest red-teaming research and findings.
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Aioga 编辑摘要
Anthropic 与 Andon Labs 合作开展无人机演示和评估，并形成 Drone-Bench。该基准测试 AI 代理在室内操控四旋翼无人机定位、识别并跟随指定人员的能力，软件化评估用于复现相关任务。
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