Breaking long robot tasks into independently explored subtasks with explicit transition management reduces exploration cost from exponential to linear and improves success rates by 15% on benchmarks.
This paper presents BATON, a system for long-horizon robot manipulation that combines vision-language-action models with LLM planning. Instead of learning end-to-end, BATON explores individual subtasks cheaply, stores solutions in memory, and chains them together while managing transitions between subtasks using a verifier agent and handoff mechanisms.