Hybrid control algorithm ensures reliable target reaching under uncertainty
Belief-Informed Hybrid Control with Almost-Sure Target-Set Convergence
Robotics
Summary
Sometimes robots or automated systems must reach a goal even when they don’t know everything about how their controls work perfectly. The authors created a method that helps decide actions by guessing unknown parameters and ensuring each step moves the system closer to its goal most of the time. Their method looks ahead two steps, checking how new information affects choices and keeps progress towards the target even if detours are needed. Tests on simple control problems and robotic assembly simulations show their approach works better than simpler methods that ignore uncertainty.
What this means in practice
- •For robotics engineers: Control robots precisely when environment parameters are uncertain, ensuring they reach targets reliably despite unknown dynamics.
- •For automation system designers: Build automated systems that adaptively explore and learn control parameters online to complete complex tasks under uncertain conditions.
Authors
Clinton Enwerem, Saleh Kemal, John S. Baras, Calin Belta
Abstract
Controlling a hybrid system to a target set under parameter uncertainty can require informative actions that temporarily drive the system state away from the specified set. We propose a belief-informed dual-control algorithm that combines belief-space receding-horizon selection with an expected-decrease constraint on a nonnegative target-set progress function. To accommodate exploratory deviations, a scalar controller state bounds the constraint's cumulative slack. Our algorithm's two-step lookahead selection uses predicted observations to update the parameter belief before evaluating the subsequent action's admissibility. Under distance-comparison bounds, correct conditional prediction, and recursive feasibility, we prove almost-sure target-set convergence at decision times and bound both the sum of expected progress-function values and the expected neighborhood-entry time. Upper confidence bounds extend these convergence guarantees to bounded model samples with summable error probabilities. In planar regulation with an unknown control direction, we verify recursive feasibility: the proposed two-step selection reduces the Euclidean state norm below 0.01 within 11 decisions for either sign, whereas a myopic one-step selection loses admissibility immediately after zero input. In a simulated bimanual assembly task, our algorithm's one-step implementation uses clearance feedback to complete the assembly under nominal friction, yielding a 96.4% lower infinity-norm relative-position error than the reference-tracking baseline at the method's completion time. At lower friction, its posterior conditioning successfully detects an empty admissible set, whereas a fixed-prior alternative admits an action that violates the conditional decrease constraint. Project page: https://clintonenwerem.com/belief-hybrid-control/.