Manipulation

Long-Horizon Manipulation

Long-horizon manipulation is the execution of tasks composed of many sequential, interdependent steps, such as cooking a meal or assembling furniture, where success requires every stage to succeed and errors compound over minutes of execution. Approaches include hierarchical decomposition with task planners or LLMs over skill libraries, subgoal generation, and end-to-end policies trained on long demonstrations, with recovery behaviors to correct intermediate failures.

Why it matters for physical AI

Compounding error is the central obstacle to economically useful autonomy: a 95 percent-reliable skill fails most twenty-step tasks, so progress on long horizons directly gates real deployment value.