Plover is a plan-centric vision-based GUI automation system that externalizes task plans and replanning as inspectable artifacts, enabling explicit supervision and localized correction.

This paper proposes a maneuver-response model for safe interaction between humans and autonomous UAV systems, treating operator inputs as bounded maneuver requests.

This paper presents Red Light, Grey Zone, a web-based interactive narrative prototype to elicit ethical reflection on autonomous driving from non-experts.

This paper proposes a theoretical model of autonomy surrender in human decision-making environments due to AI integration, including mechanisms of cognitive bandwidth depletion and recovery.

SIRI introduces a self-internalizing reinforcement learning framework that allows LLM agents to autonomously discover and integrate reusable skills directly into their core policy, significantly impro…

This paper identifies and validates eight core UX principles for human-AI agent interaction in the workplace using a multi-method approach.

This paper proposes shifting the focus of AI research from isolated computational outputs to interaction dynamics, establishing 'Interaction-Centered Intelligence' as the primary framework for underst…

This paper proposes an ontological representation for cooperative affordances in social robotics, enabling agents to extend their action possibilities through interaction.

The paper proposes Agentic ASR, a closed-loop framework that treats ASR as a multi-turn refinement task, significantly improving semantic accuracy over traditional token-level metrics.

This paper analyzes incidents involving autonomous vehicles (AVs) and identifies gaps in current safety paradigms, suggesting the need for human-interactive autonomy.

The paper introduces MindClaw, a closed-loop framework that enables embodied agents to perform real-time mental-state reasoning and intervene with precision, significantly outperforming standard VLM b…

The paper introduces AgenticRL, a self-refining reinforcement learning framework that uses a multimodal GPT agent to automatically design, refine, and deploy reward functions for complex UAV navigatio…

This paper synthesizes expert knowledge from a workshop to provide a comprehensive framework and best-practice guidelines for developing high-quality reinforcement learning environments for autonomous…

This study explores why and how users customize social AI, identifying motivations and contributions to a closer relationship.

The paper introduces HLL, a benchmark that tests if multimodal agents can successfully substitute for human verification (like CAPTCHA) in complex, real-world workflows, finding that current agents ar…

This paper proposes a definition for 'AI-nativeness' in systems, based on an AI agent's authority over system decisions.

This survey provides a comprehensive, structured review of safety research in Embodied AI, analyzing attacks and defenses across the entire embodied pipeline to guide the development of safe, robust,…

This paper provides a comparative framework analyzing the distinct security and privacy risks inherent in virtual and robotic assistive systems, culminating in design recommendations for trustworthy t…