A Formal gatekeeper Framework for Safe Dual Control with Active Exploration

2025-10-07

AI Summary

Existing robust trajectory planning methods under model uncertainty are overly conservative, while active exploration approaches often lack formal safety guarantees or fail to rigorously quantify information gain. Method: We propose a formal dual-control framework that jointly integrates robust planning and safety-constrained active exploration: information-gathering actions are activated only when online verification confirms they explicitly reduce either task cost or parameter uncertainty—without compromising safety. Contribution/Results: Our key innovation extends the gatekeeper architecture by unifying formal safety verification, robust motion planning, and uncertainty-aware online dual control. Evaluated in quadrotor simulations, the method generates trajectories that are simultaneously safe, informative, and cost-efficient, achieving significantly faster convergence of parametric uncertainty compared to baseline approaches.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, yet ignores uncertainty reduction and leads to overly conservative behavior. Actively reducing uncertainty on-the-fly during a nominal mission defines the dual control problem. Most approaches address this by adding a weighted exploration term to the cost, tuned to trade off the nominal objective and uncertainty reduction, but without formal consideration of when exploration is beneficial. Moreover, safety is enforced in some methods but not in others. We propose a framework that integrates robust planning with active exploration under formal guarantees as follows: The key innovation and contribution is that exploration is pursued only when it provides a verifiable improvement without compromising safety. To achieve this, we utilize our earlier work on gatekeeper as an architecture for safety verification, and extend it so that it generates both safe and informative trajectories that reduce uncertainty and the cost of the mission, or keep it within a user-defined budget. The methodology is evaluated via simulation case studies on the online dual control of a quadrotor under parametric uncertainty.

Problem

• Planning safe trajectories under model uncertainty with active exploration
• Ensuring safety while reducing uncertainty through verifiable improvements
• Integrating robust planning with formal safety guarantees for dual control

Innovation

• Framework integrates robust planning with active exploration
• Exploration pursued only when verifiably improving without safety compromise
• Gatekeeper architecture generates safe informative trajectories reducing uncertainty