Adaptive self-supervised learning for real-time problem solving in autonomous systems.

Forecasting in Offline Reinforcement Learning for Non-stationary Environments

Data-driven optimization: efficient adaptive learning for self-driving laboratories

It is shown how moving from BO to partitioning-based algorithms can substantially improve data efficiency, scale gracefully to dozens of process variables, and handle the constraints and noise that characterize realistic experimental campaigns.

Engineering Self-Adaptive and Autonomous Systems: A Critical Survey and an Adaptation–Assurance Framework

: Self-adaptive systems (SAS) autonomously reconfigure in response to uncertain environments and evolving requirements. Two decades of research have produced a rich but fragmented body of work spanning architecture, machine learning, uncertainty management, and verification, each strand typically surveyed in isolation. This paper makes three contributions. First, it provides a critical survey, rather than a catalogue, of foundational and contemporary SAS methodologies, organized using the reporting principles of established systematic-review guidelines. Second, it introduces an original synthesizing artifact, the Adaptation–Assurance Gap Map, which positions each class of adaptation mechanism (rule-based, control-theoretic, search-based, model-based, reinforcement learning, deep predictive, federated, and LLM-assisted) against the maturity of assurance evidence routinely available for it, exposing systematically under-assured combinations. The map is governed by an explicit rating rubric, complemented by a technique-maturity model that includes an assurance-cost dimen-sion. Third, it positions the framework against prior SAS taxonomies and converts the findings into a gap-to-research traceability matrix with concrete, falsifiable directions. Using exemplars (DeltaIoT, SWIM, and an ML-component retraining case), we show that the field’s principal weakness is not a shortage of adaptation mechanisms but a shortage of assurance that scales with them in scope, tempo, and cost.

DynBudget: Adaptive Risk Calibration and Dynamic Budgeting for Safe Reinforcement Learning Under Distribution Drift

Ensuring safety constraint satisfaction in non-stationary Reinforcement Learning (RL) environments remains a major challenge, as distribution drift can quickly invalidate previously calibrated risk estimates. To address this issue, we propose DynBudget, a closed-loop Safe RL framework integrating a learned safety critic, temperature-calibrated risk estimation, and a dynamic safety budget. By jointly adjusting the calibration temperature and the intervention threshold based on real-time violation feedback, the safety shield enables rapid adaptation to environmental drifts. We carried out experiments in two stages to fully evaluate the adaptation mechanism as well as the practical robustness of DynBudget. First, we studied the framework in a number of discrete MiniGrid contexts, and then we tested it on various continuous robotic tasks in Safety-Gymnasium. Our empirical results indicate that our approach is able to effectively reduce safety violations, shorten recovery time after a drift, and preserve competitive task performance when faced with abrupt changes in hazards or physical perturbations. Finally, we show that shielding with dynamic budgets is an interpretable and viable approach to Safe RL in autonomous systems.

Learning from the Test: Self-Referential Differential Testing for Deep RL Agents

Delta (Differential Testing for DRL Agents) is proposed, a novel and comprehensive framework that automatically identifies both safety-critical and optimality bugs in DRL agents and investigates the effectiveness of three offline RL algorithms in generating challenger agents.

Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks

HYPER-GNC, a multi-task reinforcement learning framework in which a hypernetwork maps physics-informed task embeddings to the weights of a shared actor-critic policy, enabling a single compact controller to master four distinct GNC tasks: velocity tracking, docking, inspection, and navigation with obstacle avoidance.