Robust AI-HRI in Uncontrolled Environments: Sensor Noise, Human Unpredictability, and the Sim-to-Real Deployment Gap

AI-powered robots increasingly leave the controlled conditions of the lab for busy homes, public spaces, care settings, and unstructured industrial floors. In these uncontrolled environments, systems that perform well on benchmarks often degrade sharply: sensors return noisy or incomplete data, people behave in ways no training distribution anticipated, and policies tuned in simulation fail to transfer to physical hardware. Bridging this gap between demonstrated capability and dependable real-world operation is now the central obstacle to deploying interactive robots at scale. This Research Topic focuses specifically on the robustness of AI-driven human-robot interaction (HRI) under these conditions, rather than on HRI capability in general.We organize the Topic around three tightly linked challenges. First, perception under sensor noise: maintaining reliable human perception, action recognition, and scene understanding when vision, depth, and audio inputs are degraded, occluded, or miscalibrated. Second, human unpredictability: enabling robots to anticipate, detect, and recover from out-of-distribution human behavior, and to remain safe and legible when people act in unexpected ways, including being “influence-aware” of how the robot’s own actions reshape human behavior. Third, the sim-to-real deployment gap: closing the distance between high-level reasoning (e.g., large language models and learned policies) and reliable physical execution, including domain randomization, transfer learning, runtime monitoring, and uncertainty-aware control. A recurring theme is quantifying and validating robustness, moving beyond single-environment demonstrations toward evidence that systems hold up across realistic, variable conditions.We welcome original research, methods, datasets/benchmarks, and critical perspectives. Contributions should address robustness, failure handling, or deployment of AI-HRI in real or realistically uncontrolled settings, and should report how performance is affected by, and recovers from, noise, distribution shift, or transfer. Themes include, but are not limited to:- Robust multimodal perception and sensor fusion under noise, occlusion, and degraded inputs- Action and intention recognition that remains reliable out-of-distribution- Anticipating and recovering from unpredictable or unsafe human behavior- Influence-aware motion and interaction planning in crowded or dynamic spaces- Sim-to-real transfer methods: domain randomization, system identification, transfer/meta-learning- Bridging LLM/high-level reasoning and low-level physical execution under real-world constraints- Uncertainty quantification, runtime monitoring, and fault detection for deployed HRI systems- Benchmarks, datasets, and reproducible evaluation protocols for robustness and sim-to-real performance- Safe, fluid collaboration validated in field or in-the-wild deployments Topic editors: Alessandro Freddi, Sabrina Iarlori, Vicente Ferreira De Lucena Junior, Karameldeen Omer Source: https://www.frontiersin.org/research-topics/84868/robust-ai-hri-in-uncontrolled-environments-sensor-noise-human-unpredictability-and-the-sim-to-real-deployment-gap