robotics
The Robot Learns How Long This Moment Can Wait
A metareasoning policy changes the planner's computation budget with the state instead of fixing one delay for every maneuver.
Summary
A metareasoning policy changes the planner's computation budget with the state instead of fixing one delay for every maneuver.
Planning while moving creates a physical bargain: more computation may improve the plan, but the robot must continue open-loop until that answer arrives. This work casts the budget itself as a semi-Markov decision. A learned policy observes the current state, executing plan and inexpensive planner statistics, then chooses both the action commitment and time available to the solver. Across ground navigation, agile quadrotor flight and contact-rich manipulation, the authors report better performance than fixed-budget baselines and state-dependent reasoning patterns. The tested domains do not establish safe deployment in every real-time system, but they make computation time part of the control policy rather than a constant selected before the robot enters the world.
Why it matters
A metareasoning policy changes the planner's computation budget with the state instead of fixing one delay for every maneuver.
Limits and context
- The tested domains do not establish safe deployment in every real-time system, but they make computation time part of the control policy rather than a constant selected before the robot enters the world.
Key claims
A metareasoning policy changes the planner's computation budget with the state instead of fixing one delay for every maneuver.
Qualification: The tested domains do not establish safe deployment in every real-time system, but they make computation time part of the control policy rather than a constant selected before the robot enters the world.
Evidence: source-2026-10-19-016
Sources
- arXiv preprint 2610.23662, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.