SPACE: Subtask Planning with Adaptive Computational Effort

Hierarchical robot policies enable complex, long-horizon tasks by combining multimodal reasoning for high-level planning with low-level control. However, querying an expensive planner at every planning step incurs unnecessary latency, while relying solely on a lightweight planner can lead to incorrect subtask decisions when stronger reasoning is required. We introduce Subtask Planning with Adaptive Computational Effort (SPACE), a two-step framework that pairs a lightweight local planner with a compute-heavy frontier planner to decide when to update a subtask and when stronger reasoning is needed. The local planner first predicts a binary transition token; if it indicates retention, decoding stops and the robot continues executing the current subtask. Otherwise, the local planner predicts the next subtask, and a confidence-based router either accepts that prediction or invokes the frontier planner. To make that choice, the router considers token probabilities and how much plausible alternative predictions alter the proposed subtask’s meaning or numerical details. Compared with a hierarchical baseline that predicts a full subtask at every planning step, SPACE raises task success from 40.6% to 50.3% on RoboMME and from 28.6% to 38.5% on RoboDojo, while reducing high-level planning time by 60.7% and 31.6%, respectively. On three challenging real-world manipulation tasks, SPACE also outperforms the hierarchical baseline in task success, highlighting its effectiveness across diverse reasoning demands.