Increasing demand for precise and reliable control in complex scenarios has led to the development of increasingly sophisticated controllers, including data-driven approaches employing closed box models and mathematically rigorous yet complex designs. This complexity highlights the needs for explainable control that can provide human-understandable insights into controller behavior.
arXiv:2607. 21209v1 Announce Type: cross Abstract: In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal.
arXiv:2607. 11119v1 Announce Type: cross Abstract: Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control.
arXiv:2607. 13655v1 Announce Type: new Abstract: Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios.
arXiv:2610.08815v1 Announce Type: new Abstract: Agentic AI-enabled automation cannot be safely deployed in high-stakes environments on probabilistic reasoning alone. A recurring risk is epistemic dri...
arXiv:2607. 22609v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for decision-making and reasoning tasks, yet their potential as controllers for physical systems remains largely unexplored.
arXiv:2509.15959v2 Announce Type: replace-cross Abstract: Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque de...
arXiv:2607. 01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision.
arXiv:2606. 00090v1 Announce Type: cross Abstract: Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential actions.
The paper introduces a synthetic ground‑truth framework for evaluating explainable AI (XAI) methods, addressing the lack of reliable evaluation procedures. By using controlled interventions to create datasets where the importance of input components is known, the framework generates ground‑truth explanations that align with the model’s actual decision process. The authors apply this approach to binary images, tabular data, and time series, and find that nine popular XAI methods exhibit significant limitations, underscoring the need for intervention‑based benchmarks.
The paper defines an autonomous agent as a system that maintains task goals and viability within explicit identity and authority boundaries, continuously acting with world and self-models, and adjusting control mechanisms through feedback. It traces the evolution from homeostasis to biological autonomy and identifies six operationalization lines, culminating in a one-boundary, four-loop framework that organizes task execution, self-maintenance, metacontrol, and governed learning. Five simulation experiments test permission constraints, road-closure response, health maintenance, capability re-estimation, and cross-task learning, offering preliminary evidence for the feasibility of these mechanisms while noting the need for broader testing.
The paper introduces AI Control Scientist (AICS), a large language model–driven agent that automatically generates optimized controllers from language design requirements. AICS consists of a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show that AICS outperforms existing automated baselines in design success rate and optimization efficiency, demonstrating its potential to shift control system design from human‑driven to agent‑driven approaches.
arXiv:2606. 16786v1 Announce Type: new Abstract: Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short.