Wellness Robots and the Path to Full Autonomy

Mohammad H. Mahoor; Matthew Hessler; Lombe Chileshe; Ola Alsaadi; Eshrat Emamian; Kerstin S. Haring; Richard M. Voyles

cs.RONov 6, 2026

Wellness Robots and the Path to Full Autonomy

Authors: Mohammad H. Mahoor, Matthew Hessler, Lombe Chileshe, Ola Alsaadi, Eshrat Emamian, Kerstin S. Haring, Richard M. Voyles

Organizations: Ritchie School of Engineering and Computer Science, University of Denver, Denver, Colorado, USA · DreamFace Technologies LLC, Denver, Colorado, USA · Department of Engineering, University of Texas at Arlington, Arlington, Texas, USA

Abstract

This paper introduces the autonomous wellness robot as a distinct category of socially assistive system, defined by its capacity to continuously assess and respond across the seven ICAA dimensions of wellness (see Figure 2). We propose the Care Robot Autonomy Scale (CRAS): a six-level taxonomy (L0-L5) modeled on the automotive automation standard SAE J3016 and evaluated across four wellness-aligned dimensions. We present Ryan, developed initially at the University of Denver and commercialized by DreamFace Technologies, as a case study. NIH/NIA-funded longitudinal evidence (N = 18; 887 hours) shows statistically significant cognitive improvement (SLUMS +2.61, p = 0.015) and a clinically meaningful directional reduction in depressive symptoms (PHQ-9 -2.50, p = 0.073), with no novelty effect. We position Ryan today at CRAS Level 1-2, with emerging Level 3 capabilities in some dimensions, and outline a three-phase roadmap to L5 (full autonomy) by the early 2030s.

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The Autonomous Agency Scale: A Behavioral Framework for Measuring Self-Directed Behavior in AI Systems

Samuel Presgraves

Independent researcher.

AI Agent Evaluation AI Agent Benchmarks Automated Agent Evaluation

Open paper AskPDF arXiv

Existing AI measurement frameworks quantify cognitive capability, task automation, or catastrophic risk, but none measure autonomous agency: the extent to which a system behaves in a self-directed way. A system can saturate capability benchmarks while remaining entirely reactive, acting only when prompted and ceasing all activity when a task completes. We introduce the Autonomous Agency Scale (AAS), a behavioral framework that scores AI systems on a 0-5 lexicon across seven dimensions of agency: cognitive autonomy, temporal persistence, environmental agency, social agency, creative agency, self-awareness, and goal formation, each operationalized by falsifiable threshold tests. Every dimension is scored in two temporal bands: an Active band covering engaged, user-initiated activity, and an Ambient band covering idle periods. Ambient Level 4 is gated by the Idle-Gap Test, a counterfactual criterion (remove all triggers and observe whether internally derived activity persists) that separates self-direction from scheduled rule-following. We apply the scale to six contemporary systems spanning task agents (Claude Code, Manus, Hermes), consumer assistants (ChatGPT, Siri), and a persistent companion architecture (Airi). The two-band profile quantifies a boundary that single-score frameworks conflate: task agents reach Active composites of 2.3-2.4 while scoring 0.6-1.9 Ambient, with every idle-period behavior attributable to user-configured schedules, whereas the companion architecture, evaluated longitudinally, is the only assessed system whose idle-period behavior survives trigger removal. We discuss limitations, including single-rater provenance, developer-evaluator bias on the longitudinal assessment, and the partially operationalized self-direction boundary in the Active band.

Abstract

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