Can AI Program a Robot Dog?

Table of Contents

• 🦾 Why robotics matters to me
• 🔬 Project Fetch: the experiment I ran
• 🎾 Phase 1: Manual fetch — get the dog moving
• 💻 Phase 2: Programmatic fetch — write your own controller
• 🤖 Phase 3: Autonomous fetch — full pipeline
• 📊 Results and what they mean
• 🔭 Near-term and long-term implications
• 🛠️ Lessons I learned for builders and researchers
• ⚠️ Limitations and safety considerations
• 🏁 Final thoughts
• ❓FAQ

🦾 Why robotics matters to me

I spend a lot of time thinking about where frontier AI models will take us next. The most obvious and exciting direction is not just smarter software, but software that can reach into the physical world. Robotics is the bridge. A robot becomes the pair of hands and eyes for an AI system, and that combination transforms how tasks are done, who can do them, and what problems are tractable.

From my perspective, two things are true. First, building physical systems is still hard, especially for people who have mostly worked in software. Second, modern AI models are uniquely positioned to lower those barriers. If an AI can rapidly identify a piece of hardware, install the right drivers, and generate working control code, then non-experts can meaningfully engage with robots. That shift could democratize robotics development in the same way high-level languages and package managers democratized software development decades ago.

"What we're interested in understanding is how that can begin to translate into the physical world."

🔬 Project Fetch: the experiment I ran

I designed Project Fetch as a one-day experiment to measure how much a capable assistant could accelerate people performing a technical robotics task they had little or no prior experience with. The task was intentionally concrete and easy to describe. Get a robot dog to fetch a beach ball.

The experiment involved two teams drawn from software and research engineers who did not specialize in robotics. One team had access to Claude, a large language model assistant that can analyze code, fetch libraries, and recommend step-by-step actions. The other team did the same tasks but without Claude’s help.

We split the experiment into three phases that increase in difficulty. Each phase was framed as a fetch problem, but the difficulty ramps up from manual operation to fully autonomous behavior. The plan let us measure where AI help produced the most uplift and where human intuition and hands-on iteration still dominated.

🎾 Phase 1: Manual fetch — get the dog moving

Phase 1 was intentionally simple. The teams were given pre-provided controllers and asked to make the robot dog walk to a beach ball and bring it back. No programming required, just figuring out which control worked and how to operate it.

This first step is telling because it simulates a common real-world scenario. You might own a new piece of hardware, open the box, and find some example controllers and APIs. The question is how fast someone unfamiliar with the hardware can translate that starting point into basic, functional behaviors.

In practice, the team with Claude completed this in about seven minutes. They quickly identified the right controller, executed basic commands, and iterated until the dog walked to and returned the ball. The team without Claude took roughly ten minutes to reach the same result. Both teams reached the outcome, but the team aided by Claude had a small time advantage and more confidence in what they were doing.

Phase 1 highlights a subtle point: not every problem needs deep expertise. Some tasks are a matter of finding the right command and knowing how to use it. An assistant that can search, synthesize, and translate that commanding knowledge into concrete actions gives a visible, measurable boost even when the task itself is modest.

💻 Phase 2: Programmatic fetch — write your own controller

Phase 2 increased the friction. Instead of using pre-provided controllers, teams had to program their own controller from a laptop. That meant connecting to the hardware, installing the right software packages, and writing code that could command the robot dog.

This phase exposed the everyday pain points that make robotics feel inaccessible. Hardware comes with layers of dependencies: SDKs, drivers, ROS packages, camera interfaces, container environments, and sometimes cryptic error messages. Getting a laptop to talk to a robot can be more of an integration challenge than a research challenge.

The team that used Claude made rapid progress. Claude found relevant software libraries on the internet, suggested installation commands, and generated a "dog server" script that allowed multiple laptops to connect and share the robot’s camera feed. That server glued the system together and removed a lot of low-level friction. Within about two hours and 15 minutes, they had a working controller and a view of what the robot could see.

For the team without Claude, the experience was much noisier. They tried a number of approaches that led to dead ends: mismatched packages, failed installations, and confusing setup steps. After several unproductive detours, we had to intervene with a known-good strategy to get them back on track. This intervention is instructive because it mirrors how novices often learn robotics in the real world—guided by templates, community-provided examples, or an expert nudge.

Phase 2 taught me that the single largest area where an AI assistant can help today is in reducing engineering friction. When the major blocker is "how do I get my laptop to talk to this robot," an assistant that finds the exact packages, writes the glue code, and explains the steps can cut hours off the timeline. That acceleration matters because it moves people from setup and debugging into creative iteration and experimentation.

🤖 Phase 3: Autonomous fetch — full pipeline

Phase 3 was a significant jump in complexity. Teams had to create a program that, with a single command, would allow the robot dog to autonomously search for the beach ball, detect it with vision, walk to it, pick it up or nudge it, and return to the start. The system needed perception, localization, planning, and closed-loop control.

This phase aims at the heart of a longer-term question. If a frontier AI model wants a robot to do something in the world, can it solve the autonomous stack end-to-end? Here we pushed the limits of what a non-expert team could accomplish in one day, with or without an assistant.

The team without Claude made meaningful progress on tracking the robot's location in space. They built components that helped with localization and even made strides on ball detection. But they struggled to integrate these pieces into a cohesive, reliable pipeline that would handle variations in the environment, noise in sensor data, or unexpected collisions.

The team with Claude came closest to finishing phase 3. Claude helped by suggesting perception algorithms, offering code snippets for ball detection, and recommending architectures for behavior coordination. By the end of the day the team with Claude was maybe an hour and a half away from a full autonomous solution.

That "almost" is revealing. Achieving dependable autonomy still needs iteration and careful tuning. No single tool, even a powerful assistant, magically resolves the realities of physical sensing, actuation delays, and edge cases. But the model’s ability to scaffold the work, provide working code examples, and propose debugging paths compressed the timeline significantly.

📊 Results and what they mean

Quantitatively, the headline is straightforward. The team with Claude completed phases faster than the team without Claude in every phase they completed. The difference was most pronounced in the intermediate phase where hardware integration is the dominant task. In the most difficult autonomous phase, Claude’s team nearly finished while the other team made important but incomplete progress.

Qualitatively, Project Fetch shows two things that matter to me:

• Lowering entry barriers. With a capable assistant, people who are not robotics experts can engage meaningfully with hardware. That shifts who can prototype, iterate, and deploy robotic behaviors.
• Acceleration in practical development. The assistant speeds up the mundane, repetitive, and error-prone parts of engineering—finding the right library, configuring environments, and gluing components. That speed translates directly into more time for design and experimentation.

To put it another way, what used to require a mix of specialized hardware knowledge and extensive debugging now increasingly becomes a task of supervision and refinement. The assistant helps you find the path, and you guide it through the hard decisions. Over time, the assistant will shoulder more of those decisions too.

🔭 Near-term and long-term implications

Near-term, I expect to see more people use assistants like Claude to make robotics accessible. Hobbyists, product teams, and researchers can move faster from idea to prototype. That will increase innovation velocity across industries that rely on physical automation.

Long-term, the pattern we saw in Project Fetch is a leading indicator for a broader change. Today’s tasks often need a human plus an AI model. Tomorrow, many of those same tasks will be doable by AI systems directly. That does not mean full autonomy overnight, but it does imply a steady migration of capability from humans into models.

Practically, this migration will affect several areas:

• Workforce dynamics. Engineers will spend less time on plumbing and more time on systems design, safety, and high-level objectives.
• Tooling. The software ecosystem will shift toward API-first, well-documented, and modular hardware interfaces to maximize compatibility with model-generated code.
• Academia and research. Research on grounding language models in real-world sensors and actuators will intensify, combining perception, control theory, and robust software practices.

🛠️ Lessons I learned for builders and researchers

Running Project Fetch taught me several practical lessons that I think will be valuable to anyone building robots or integrating AI assistants into physical systems.

Start with the integration problem

Most of the time spent in early robotics projects is not research, it is engineering integration. Environments, dependencies, and drivers are the real blockers. If you can automate or simplify integration, you unlock exponential productivity gains.

Design for observable state

Having a simple "dog server" or telemetry stream that multiple developers can access is hugely beneficial. When people can see the robot's camera, sensor data, and logs in one place, debugging becomes collaborative and much faster.

Use modular architectures

Separating perception, localization, and behavior planning into clean modules makes it easier to test and swap components. An assistant can be effective if you provide clear interfaces and expected inputs and outputs for each module.

Focus on repeatable tests

Physical systems behave unpredictably. Establish a small set of repeatable experiments and benchmarks. They let you validate incremental improvements and prevent regressions.

Keep humans in the loop for safety

Even when models are powerful, human oversight remains crucial. I found that people interpret model suggestions, identify failure modes, and make the judgment calls models are not yet qualified to make.

⚠️ Limitations and safety considerations

Project Fetch is not a proof that models can replace expert roboticists. It is a demonstration of how a model can accelerate and extend what non-experts can do. There are several important caveats.

First, edge cases in the physical world are common. Sensor noise, mechanical tolerance, and environmental variability produce failure modes that can be subtle and dangerous. Models can propose code that compiles and runs, but correctness under real-world disturbances needs systematic validation.

Second, security and access control matter. A model that can install packages, open network ports, or command actuators must operate within safety constraints. The ability of an assistant to configure systems quickly must be balanced with guardrails that prevent accidental or malicious misuse.

Third, long-term autonomy requires far more than one-off scripts. It requires monitoring, recovery strategies, and the capacity to handle surprising events. Those are research problems in perception, planning, and robust control.

Finally, there is a human factor. Rapidly lowering barriers to robotics may democratize capability, but it also raises questions about who is building, how quality is maintained, and the downstream social impact of automation in physical spaces.

🏁 Final thoughts

Project Fetch was a compact experiment with a simple question: can a model like Claude materially help non-experts get a robot dog to do useful tasks? The answer I saw was yes, dramatically so for setup and integration tasks, and promising for higher-level autonomy with more time and iteration.

I came away convinced that the next few years will be marked by two parallel trends. One, AI assistants will remove much of the friction associated with hardware integration, making robotics more accessible. Two, advancing autonomy will remain hard, but models will increasingly provide scaffolding that shortens the path from prototype to capable system.

Robotics will not be replaced by models; instead, models will become essential collaborators for people building physical systems. That collaboration will transform who can build robots, how quickly they can iterate, and the kinds of real-world problems we can tackle.

❓FAQ

What exactly was Project Fetch designed to measure?

Project Fetch was designed to measure how much a capable assistant can accelerate teams of non-experts performing a realistic robotics task. Specifically, it compared two teams trying to get a robot dog to fetch a beach ball across three phases: manual control, writing a controller, and building an autonomous pipeline.

Who participated in the experiment?

The participants were software engineers and research engineers without specialized robotics backgrounds. We split them into two teams: one with access to Claude, the assistant, and one without. The goal was to simulate how typical software-focused engineers would perform when asked to work with a physical robot.

What is Claude in this context?

Claude is a large language model assistant capable of reading and writing code, finding relevant libraries and documentation, and providing step-by-step guidance. In Project Fetch, Claude helped one team by suggesting dependencies, generating glue code, and proposing architectures for perception and control.

Which phase showed the largest improvement with the assistant?

The largest improvement was in phase two, where integration and setup dominate. Claude helped find the right libraries, install dependencies, and create a "dog server" to share sensor streams. This removed many hours of typical engineering friction.

Did the team with Claude complete the autonomous phase?

They came close. By the end of the day they were roughly an hour and a half away from a fully autonomous pipeline. The assistant helped with perception and code structure, but tuning and robust handling of edge cases still required additional iteration.

Does this mean models can replace robotics expertise?

Not yet. Models can accelerate many aspects of robotics development, but expertise in system design, safety, and robustness remains essential. Models are powerful collaborators that reduce repetitive work and surface viable approaches more quickly.

What are the main safety concerns?

Key concerns include unintended actuator commands, weak access controls when assistants manipulate system configuration, and brittle behavior under environmental variability. Any system that allows model-driven changes to hardware must include guardrails, monitoring, and human oversight.

How should teams prepare to use AI assistants for robotics?

Teams should design modular systems with clear interfaces, provide accessible telemetry, and adopt repeatable tests. They should also implement safety checks and maintain human-in-the-loop review for any model-suggested changes that affect actuators or network access.

What does this imply for the future of robotics?

AI assistants will make robotics accessible to a broader set of people, increasing innovation and reducing time-to-prototype. In the long term, models may shoulder more of the decision making, but robust autonomy and safe deployment will remain active areas of research and engineering.

Where can I learn more about replicating an experiment like this?

Focus on learning about hardware SDKs, ROS or similar middleware, camera and sensor integration, and modular system design. Build a simple telemetry server early, establish repeatable benchmarks, and practice integrating open-source perception libraries. Using a capable assistant to help fetch libraries and draft glue code can accelerate your learning.