# Learning Robot Metareasoning: Adaptive Computation Budgets for Real-Time Control

- Source: arXiv cs.RO
- Published: 2026-10-19T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/z45v3b10hr6bjwyz8xnwy067q
- Original: https://arxiv.org/abs/2610.23662

## Summary

The arXiv paper Learning Robot Metareasoning proposes modeling the determination of the optimal computation budget as a semi-Markov decision process (SMDP), in which a single decision jointly determines the robot's action commitment duration and the time allocated to the solver. The authors trained a reinforcement learning policy conditioned on the current state, the executing plan, and low-cost planner statistics. Across three task types—ground robot navigation, agile quadrotor flight, and contact-rich manipulation—the policy adjusts its budget according to scene demands and outperforms fixed-budget baselines.