Gustav Sir | Searcharxiv

arXiv2025

Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations

Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots, unmanned aerial vehicles, embedded control devices, and a number of other realizations of cybernetic/mechatronic implementations of intelligent autonomous devices. In this paper, we consider a stylized version of robotic care, which would normally involve a two-level Reinforcement Learning procedure that trains a policy for both lower level physical movement decisions as well as higher level conceptual tasks and their sub-components. In order to deliver greater safety and reliability in the system, we present the general formulation of this as a two-level optimization scheme which incorporates control at the lower level, and classical planning at the higher level, integrated with a capacity for learning. This synergistic integration of multiple methodologies -- control, classical planning, and RL -- presents an opportunity for greater insight for algorithm development, leading to more efficient and reliable performance. Here, the notion of reliability pertains to physical safety and interpretability into an otherwise black box operation of autonomous agents, concerning users and regulators. This work presents the necessary background and general formulation of the optimization framework, detailing each component and its integration with the others.

math.OC↗

arXiv2025

"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"

Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising computational techniques to reveal ``true'' causality. In this paper, we critique the premise of causal learning by considering the epistemology of causality across disciplines, applying the Ordinary Language method of an anthropological investigation of customary word use in reasoning about cause and effect in the real world. We observe that although cause-and-effect semantics vary between scientific domains, they maintain a consistent central function of describing the mechanisms underlying forces most salient to the systems under research. A critical distinction is the degree to which the mathematical representations of the models used for statistical analysis exhibit a faithful correspondence to expert knowledge in mechanism. We demarcate 1) physics and engineering as domains wherein mathematical models are sufficient to comprehensively describe causality, in contrast to 2) biology, which studies open and irreducible systems with mechanisms crossing scales through emergence, and 3) the social sciences as suffering from compounding difficulties for precision but providing, through Hermeneutics, the potential for subjective phenomenology yielding findings instrumentally useful to individuals. We posit the greater the discrepancy between expert-defined models and complete characterization of mechanism, the more that epistemic virtue requires that definitive causal claims regarding phenomena can only come through an agglomeration of consistent evidence across multiple domains. Exercising greater caution in communicating the degree of certainty evidence provides, especially demanding restraint in the face of incentives to overstate research conclusions, is the only durable solution to modern science's collective action problems.

cs.LG↗