Claude Plays Robotics

How Claude Performs on Robotics Tasks \ Anthropic Frontier Red Team Claude plays robotics Jul 9, 2026

Do language models’ strengths transfer to robotics, a domain which requires the synthesis of logical skills and precise 3D understanding? Can a model perceive a scene, understand a particular robot’s state, and issue actions that reliably effect change in the physical world? We ran tests to find out. We gave several language models control over a range of robot bodies—including classic control toys, a simulated quadruped and humanoid, a robotic arm, and a real Unitree Go2 (the quadruped robot of Project Fetch ). We gave the models a range of ways to control them, which varied in their abstraction (that is, how “high-level” their instructions are): from directly commanding motor torques (at the least abstract end), to writing controller code, to training a controller from scratch with reinforcement learning, to providing high-level steering instructions to a pretrained robot policy (a separate neural network that turns high-level commands into coordinated joint movements). We tested models’ performance in three areas: on classic control problems (like balancing a pendulum), locomotion and navigation (getting legged robots to balance, walk, and move through space), and manipulation (using a robotic arm to grasp and move objects). Models are getting better at robotics quickly, but we found that how capable they are depends heavily on how they are connected to the robot—which of the control methods they used. When they must drive the joints themselves they mostly fail. But when they supervise a pretrained controller or use simple orientation tools, they can complete real navigation and manipulation tasks. Some forms of embodiment remain unwieldy and difficult to control, but newer models, especially, are substantially stronger at adjusting their strategies and converting image and sensory understanding into appropriate actions across domains. This has important implications for the safe development and deployment of language models. Today’s frontier models cannot control humanoid robots without a pretrained policy, but newer models have made real gains in direct manipulation and high-level policy control across the humanoid and quadruped embodiments we tested. We expect future models to be even better. Put concretely: a general-purpose chat model with no robotics training can already, on a good run, write and download its own tools to slowly walk a quadruped through a maze or pick a plate off a counter and set it on a stove, and the gap in reliability is closing with each model generation. Composite score on Embody (our benchmark suite) by model, stacked by control interface. The composite is a normalized average across every robot body ("embodiment") and task in the suite except for high-level locomotion; higher bars mean broader physical competence. Summary of findings A model's robotics score depends as much on the robot body and the control interface as on the model itself. The same model can look weak or strong depending on whether it is setting motor torques directly, writing a Python controller, supervising a pretrained policy, or training its own policy with reinforcement learning—each of which is a different way of the model completing the same task. For the most challenging bodies to control (the humanoid in particular), today's models only get traction at the higher-abstraction interfaces, in which a pretrained policy handles the low-level physics. Models are improving at robotics, but unevenly. The most consistent performance improvements between model generations are on the high-level interfaces. Direct low-level control is also improving, but much less consistently: some new models clearly improve over their predecessors, but others don’t. On locomotion tasks, frontier models can now perform limited but meaningful whole-body control. Newer models make progress on low level control quadruped standing, balancing, and walking—and show weaker but measurable gains on humanoid balancing. Using pretrained policies and perception tools, they can even navigate simple environments. However, models still fail at tasks that require stable spatial memory, self-localization, or long open-loop plans. With low-level manipulation methods, models are beginning to produce useful local physical behavior, even though full task success remains rare. Newer models are better at reaching objects, making contact, and grasping. However, they only complete the full task a small percentage of the time (from 0 to 5.5%). With high-level manipulation methods, newer models are more successful when using pretrained policies. Vision-language-action (VLA) scaffolds—pretrained policies that map camera images and an instruction directly to robot-arm motions—raise models’ manipulation performance far above direct control. Newer models are also becoming increasingly good supervisors of those policies: they are better at recognizing when a proposed action will fail, less likely to defer to the VLA indiscriminately, and therefore make further progress on manipulation tasks. Supervising the policy still costs some performance—the combined system does worse than the VLA running on its own—but the best supervisors now close most of the gap. That does not mean supervision is useless: earlier models destroy most of the policy's value, the best of current models recover most of it, and on tasks the VLA cannot do alone the strongest models already provide net uplift.

Simple settings We begin by evaluating robot-relevant capabilities on a set of simple control tasks, including classic reinforcement learning (RL) problems such as balancing an inverse pendulum and controlling a hopper. Although these are simplified environments, we believe they require the model to reason about dynamics, cause and effect over time, and basic physics—capabilities that are important precursors to more general physical understanding. In these low-dimensional environments, sensory data provides nearly all the necessary information, allowing them to be solved with little to no visual input. This kind of low-dimensional control also arises in some real-world settings, such as camera stabilization. We evaluate models through four control interfaces, all in the simulation engine Mujoco. (Throughout, "classic control" names this family of toy tasks; "direct control" names one of the four interfaces below—they are independent...