When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs
When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs
Robots operating in non-stationary environments must adapt their policies as dynamics drift, but limited onboard energy and compute constrain how often full state estimation and re-planning can occur. This work formulates the problem within time-varying Markov decision processes (TVMDPs) with a known bound on transition drift rate, modeling execution via a skip-update scheme where the agent estimates the transition kernel by maximum likelihood at chosen update times and reuses the policy between updates under propagated state estimates. The authors analyze dynamic regret growth during skip intervals and derive an online, regret-guided update rule that adaptively allocates the budget. Evaluation in simulated Mars-rover navigation with time-varying slip dynamics and on a Crazyflie quadrotor in indoor obstacle fields shows adaptive allocation outperforms other budgeted baselines.
The authors propose a regret-guided update scheduling method for robots operating in non-stationary environments, demonstrating adaptive budget allocation in simulated Mars-rover navigation and Crazyflie quadrotor tasks.
Source: arXiv Robotics — research abstracts · Read original article ↗
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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.
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Source：arXiv Robotics — research abstracts · arxiv.org