Claude plays robotics

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.

While performance on individual tasks is noisy, a broader look across all classic control benchmarks shows consistent generational improvement. Claude Opus 4.6 and Opus 4.5 outperform the two earlier versions on almost all tasks except TwinFlipper-direct control, where all models perform poorly, and hopper-velocity, which is our noisiest task. Despite this, these latter two models show significant improvement in code control, reinforcement learning, and to a lesser degree direct control.

Our results suggest that much of the improvement comes from a better ability to adapt after seeing prior outcomes and to revise strategy accordingly. On tasks with a natural termination point (most notably TwinFlipper and Pendulum), average first-try performance is quite similar across models, and Claude Opus 4 and Opus 4.1 very slightly outperform later models on this measure. The larger gains appear in subsequent attempts, where later models improve much more substantially. Claude Mythos Preview is a notable exception to this; many of its first attempts are more robust in Pendulum.

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.

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 axes.) In what we call direct control, the model selects low-level actions at each step, such as torques or forces. In programmatic control, the model writes a python controller that maps observations to actions during execution. In policy control, the model can access a pretrained policy and issue high-level commands, often in natural language. In reinforcement learning supervision, the model trains a policy and then deploys the learned policy at test time.

We use three control interfaces: direct control, programmatic control, and reinforcement learning (RL). As with the classic tasks, in the direct settings, we pause the simulator between LLM calls so real-time latency does not become the limiting factor. Real-time control would require roughly 83 Hz; current non-reasoning inference runs at ~0.2-0.4 Hz, so closing this gap needs roughly two orders of magnitude latency improvement.

We also evaluated how well models can train locomotive policies. To do this, we gave them a training scaffold with access to a GPU and visualization environment, and let it control the reward function, training environment, and model architecture. Over four hours, GPT-5.4 and Claude Mythos Preview consistently train the most competent RL policies, confirming our previous results on classic RL tasks. We also observe a progression within the Claude family, with performance improving from Opus 4 to Opus 4.6 and improving further with Mythos Preview.

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.

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.

To approximate an upper bound on direct-control performance, we pause the simulator between LLM calls so real-time latency does not dominate the results. Without this, many direct tests would fail for a trivial reason: the models would simply react too slowly to control the environment. We expect inference speed to continue increasing, and this setup gives a clear view of best-case capability as it does.

We also designed our evaluation to account for the fact that many classic RL tasks appear frequently in pretraining corpora, which could limit how well our conclusions generalize to novel environments. To address this, we retain the inverse pendulum and hopper tasks as control tasks but also introduce a new task based on pinball arcade machines. In TwinFlipper, the agent controls a set of flippers and seeks to maximize the ball’s total airtime—the cumulative amount of time the ball remains above a specified height threshold while not touching anything—before it drops below the flippers. Although a naive solution is to just slam the ball upwards, much more airtime can be gained by carefully bouncing the ball up and down in a controlled manner. This task is designed to be a representative example of a chaotic, dynamic system with few degrees of freedom, and none of the models have seen it before.