Safety by Invariance, Liveness through Refinement: Heterogeneous Contract Framework for Co-Design of Layered Control

Yoshinari Takayama; Alessio Iovine; Bart Besselink; Guillaume Sandou; Adnane Saoud

eess.SYMay 5, 2026

Safety by Invariance, Liveness through Refinement: Heterogeneous Contract Framework for Co-Design of Layered Control

Authors: Yoshinari Takayama, Alessio Iovine, Bart Besselink, Guillaume Sandou, Adnane Saoud

Organizations: Laboratory of Signals and Systems (L2S), CNRS, CentraleSupelec, Paris-Saclay University, France · Bernoulli Institute for Mathematics, Computer Science, and Artificial Intelligence, University of Groningen, The Netherlands · College of Computing, University Mohammed VI Polytechnic, Benguerir, Morocco

Abstract

Real-world control systems must achieve long-horizon objectives (liveness) while respecting continuous-time safety constraints, a combination that motivates hierarchical layered control architectures (LCAs). Existing LCA research, however, lacks (i) a uniform specification language across discrete planning and continuous execution, (ii) formal guarantees that specifications are preserved when interconnecting subsystems at heterogeneous time scales, and (iii) compositional separation between layers, owing to reliance on naive input-filtering laws. This paper addresses all three gaps by importing the safety--liveness decomposition into a heterogeneous assume--guarantee framework: \emph{safety is enforced by invariance} at the continuous-time layer, while \emph{liveness is achieved through refinement} at the discrete-time layer, with inter-layer coordination formalized via vertical refinement and timing-compatibility conditions. We instantiate this contract with a novel LCA combining an MPC planner, an input-to-state stabilizing (ISS) low-level controller, and a reference-governor bridge, and validate it on a Hybrid Energy Storage System (HESS) comprising a battery and a supercapacitor.

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May 11, 2026 · cs.LGJ/K move · Enter open · S save

Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance

Adam Haroon, Erick J. Rodríguez-Seda, Cody Fleming, Tristan Schuler

Department of Mechanical Engineering, Iowa State University, Ames, IA, USA · Navy Center for Applied Research in Artificial Intelligence (NCARAI), U.S. Naval Research Laboratory, Washington, D.C., USA · Department of Weapons, Robotics, and Control Engineering, United States Naval Academy, Annapolis, MD, USA

Safe reinforcement learning (RL) typically asks

\textit{what}

an agent should do. We ask

\textit{when}

it needs to act, and show that a single policy can jointly learn control inputs and communication-efficient timing decisions under a pointwise Lyapunov safety shield. We focus on stabilization around a known equilibrium, where CARE-based LQR backups, Lyapunov certificates, and classical Lyapunov-STC are well defined, enabling clean comparison against analytical baselines. A run-time assurance (RTA) layer overrides the policy via a one-step-ahead Lyapunov prediction and a precomputed LQR backup, providing a strictly stronger guarantee than constrained MDP methods that enforce safety only in expectation. On an inverted pendulum, cart--pole, and planar quadrotor, the learned policy achieves

1.91\times

1.45\times

, and

3.51\times

higher mean inter-sample interval (MSI) than a Lyapunov-triggered baseline; a fixed LQR controller at the same average rate is unstable on all three plants, showing that adaptive timing, not a lower average rate, makes sparsity safe. A CARE-derived Lyapunov reward transfers across environments without redesign, with a single weight

w_c

controlling the stability--communication tradeoff; ablations confirm the RTA shield is essential, with its removal reducing MSI by

1.27

1.84\times

and degrading state norms. A preference-conditioned extension recovers the full tradeoff frontier from one model at

\tfrac{2}{11}

of training compute, and SAC experiments show the results are algorithm-agnostic across discrete and continuous domains. A 12-state 3D quadrotor case study extends the framework to higher-dimensional systems where classical STC is intractable, and robustness to

\pm30\%

mass variation and disturbances shows graceful degradation, with the RTA absorbing what the learned policy cannot.

Safety Constraints Lyapunov Function Deep Reinforcement Learning Inference-Time Control Runtime Enforcement Quadrotor Flight Assurance

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Abstract

Explore similar work

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