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The paper treats robot replanning as an online learning problem: it decides when to re-estimate a drifting transition model and re-plan, using dynamic regret bounds to guide the schedule. The regret analysis and the adaptive budget allocation are the main contributions, and the Mars-rover and quadrotor experiments are small-scale validations. Readers should note that the gains are shown against budgeted baselines in two specific domains, so generalization to other settings remains untested.
When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs
arXiv:2606.16972v2 Announce Type: replace Abstract: Robots operating in non-stationary environments must continually adapt their policies as the dynamics drift, but onboard energy and compute budgets cap how often a full state estimation and re-planning step can be performed. This raises a question: \emph{when}, along a horizon, should a robot spend its limited budget? We formulate this problem in time-varying Markov decision processes (TVMDPs) with a known bound on the rate of transition drift. We model execution as a \emph{skip-update} scheme in which, at chosen update times, the agent estimates the transition kernel by maximum likelihood and computes a finite-horizon policy, and between updates reuses this policy under a propagated state estimate. We analyze the dynamic regret of this scheme and show how it grows during skip intervals in terms of the properties of the TVMDP and the skip lengths; the resulting bound answers the opening question via an online, regret-guided update rule that allocates the budget adaptively. We evaluate the rule in a simulated Mars-rover navigation task with time-varying slip dynamics and on a Crazyflie quadrotor in indoor obstacle fields. Adaptive allocation outperforms other budgeted baselines.
Title:When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs
Abstract:Robots operating in non-stationary environments must continually adapt their policies as the dynamics drift, but onboard energy and compute budgets cap how often a full state estimation and re-planning step can be performed. This raises a question: \emph{when}, along a horizon, should a robot spend its limited budget? We formulate this problem in time-varying Markov decision processes (TVMDPs) with a known bound on the rate of transition drift. We model execution as a \emph{skip-update} scheme in which, at chosen update times, the agent estimates the transition kernel by maximum likelihood and computes a finite-horizon policy, and between updates reuses this policy under a propagated state estimate. We analyze the dynamic regret of this scheme and show how it grows during skip intervals in terms of the properties of the TVMDP and the skip lengths; the resulting bound answers the opening question via an online, regret-guided update rule that allocates the budget adaptively. We evaluate the rule in a simulated Mars-rover navigation task with time-varying slip dynamics and on a Crazyflie quadrotor in indoor obstacle fields. Adaptive allocation outperforms other budgeted baselines.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2606.16972 [cs.RO]
(or arXiv:2606.16972v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.16972 Focus to learn more arXiv-issued DOI via DataCite
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