SymBridge: A Human-in-the-Loop Cyber-Physical Interactive System for Adaptive Human-Robot Symbiosis
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Abstract (arXiv)
The development of intelligent robots seeks to seamlessly integrate them into the human world, providing assistance and companionship in daily life and work, with the ultimate goal of achieving human-robot symbiosis. This requires robots with intelligent interaction abilities to work naturally and effectively with humans. However, current robotic simulators fail to support real human participation, limiting their ability to provide authentic interaction experiences and gather valuable human feedback essential for enhancing robotic capabilities. In this paper, we introduce SymBridge, the first human-in-the-loop cyber-physical interactive system designed to enable the safe and efficient development, evaluation, and optimization of human-robot interaction methods. Specifically, we employ augmented reality technology to enable real humans to interact with virtual robots in physical environments, creating an authentic interactive experience. Building on this, we propose a novel robotic interaction model that generates responsive, precise robot actions in real time through continuous human behavior observation. The model incorporates multi-resolution human motion features and environmental affordances, ensuring contextually adaptive robotic responses. Additionally, SymBridge enables continuous robot learning by collecting human feedback and dynamically adapting the robotic interaction model. By leveraging a carefully designed system architecture and modules, SymBridge builds a bridge between humans and robots, as well as between cyber and physical spaces, providing a natural and realistic online interaction experience while facilitating the continuous evolution of robotic intelligence. Extensive experiments, user studies, and real robot testing demonstrate the promising performance of the system and highlight its potential to significantly advance research on human-robot symbiosis.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2025.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2025Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesiscited by 20
- 2026ProAct: Harnessing Streaming Motion Generation and Agentic Reasoning for Real-Time Embodied Social Interactioncited by 2
- 2026ReMoGen: Real-time Human Interaction-to-Reaction Generation via Modular Learning from Diverse Datacited by 1
- 2026ReMoGen: Real-time Human Interaction-to-Reaction Generation via Modular Learning from Diverse Data1 Apr 2026
- 2026ProAct: Harnessing Streaming Motion Generation and Agentic Reasoning for Real-Time Embodied Social Interaction15 Feb 2026
- 2025Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis4 Aug 2025