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Don't Drop the BATON: Long-Horizon Robot Manipulation via Agentic Subtask Exploration and Transition-aware Memory

BATON explores robot subtasks independently, stores their solutions, and manages handoffs between them, replacing multiplicative whole-task search with additive composition.

arXiv
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Source Summary

BATON freezes the vision-language-action model and explores each subtask separately, changing estimated exploration from **T^K to T×K**. Transition-aware memory checks invocation readiness, repairs handoff state, and selects outcomes compatible with the next stage without updating parameters.

Practical Implication

The transferable agent pattern is to make subtasks independently testable and reusable, then represent their entry and exit contracts explicitly. That localizes failures and prevents a step that passes in isolation from leaving unusable state downstream.

Agent-Ready Context
BATON freezes the vision-language-action model and explores each subtask separately, changing estimated exploration from **T^K to T×K**. Transition-aware memory checks invocation readiness, repairs handoff state, and selects outcomes compatible with the next stage without updating parameters.

The transferable agent pattern is to make subtasks independently testable and reusable, then represent their entry and exit contracts explicitly. That localizes failures and prevents a step that passes in isolation from leaving unusable state downstream.

On RoboMemArena, BATON reports **+11.6% task success** and **+14.9% cumulative success** over the prior state of the art. The evidence is specific to long-horizon robot manipulation, so applicability to software-agent workflows remains an analogy rather than a measured result.
Context Map
agent#subagents#agent-memory#harness-engineering
Uncertainty
On RoboMemArena, BATON reports **+11.6% task success** and **+14.9% cumulative success** over the prior state of the art. The evidence is specific to long-horizon robot manipulation, so applicability to software-agent workflows remains an analogy rather than a measured result.