Runtime

Preview: PolicyRuntime is a planned API. The content below documents the target design.

PolicyRuntime runs a policy on robot hardware. It owns the control loop, the callback lifecycle, and the interaction between observations, inference requests, and actions.

python

runtime = PolicyRuntime.from_config("runtime.yaml")
runtime.run(duration_s=60)

Responsibilities

Component	Owns	Does not own
`InferenceModel`	model load, preprocess, inference, postprocess	robot loop timing
`Execution`	where inference runs	robot IO
`ActionQueue`	action chunks and buffering	model inference
`PolicyRuntime`	observe, request inference, send action, callbacks, timing	policy math
`Robot`	hardware connection, observations, actions	policy inference

Loop

The runtime loop follows this general pattern:

text

while running:
    observation = get_robot_state() + get_camera_frames()
    maybe_request_inference(observation)
    action = get_next_action_or_hold()
    send_action_to_robot(action)
    sleep_until_next_tick()

The exact observation structure and merging strategy may change as the API stabilizes.

Execution Modes

Mode	Where inference runs	Use
`SyncExecution(mode="single_action")`	runtime thread	simple policies
`SyncExecution(mode="chunk")`	runtime thread	chunk policies without background worker
`AsyncExecution(transport="thread")`	worker thread	avoid blocking control loop
`RemoteExecution`	remote server	robot host without policy weights

Product Workflows

HIL, recording, highlight, and DAgger should be composed through callbacks until they justify reusable runtime primitives.

python

class HILCallback:
    def before_send_action(self, action, step):
        if teleop.enabled:
            return teleop.read_action()
        return action