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Guo Han

Publications and source records attributed to Guo Han.

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I-Design: Personalized LLM Interior Designer

Interior design allows us to be who we are and live how we want - each design is as unique as our distinct personality. However, it is not trivial for non-professionals to express and materialize this since it requires aligning functional and visual expectations with the constraints of physical space; this renders interior design a luxury. To make it more accessible, we present I-Design, a personalized interior designer that allows users to generate and visualize their design goals through natural language communication. I-Design starts with a team of large language model agents that engage in dialogues and logical reasoning with one another, transforming textual user input into feasible scene graph designs with relative object relationships. Subsequently, an effective placement algorithm determines optimal locations for each object within the scene. The final design is then constructed in 3D by retrieving and integrating assets from an existing object database. Additionally, we propose a new evaluation protocol that utilizes a vision-language model and complements the design pipeline. Extensive quantitative and qualitative experiments show that I-Design outperforms existing methods in delivering high-quality 3D design solutions and aligning with abstract concepts that match user input, showcasing its advantages across detailed 3D arrangement and conceptual fidelity.

cs.AI

EgoSpot:Egocentric Multimodal Control for Hands-Free Mobile Manipulation

We propose a novel hands-free control framework for the Boston Dynamics Spot robot using the Microsoft HoloLens 2 mixed-reality headset. Enabling accessible robot control is critical for allowing individuals with physical disabilities to benefit from robotic assistance in daily activities, teleoperation, and remote interaction tasks. However, most existing robot control interfaces rely on manual input devices such as joysticks or handheld controllers, which can be difficult or impossible for users with limited motor capabilities. To address this limitation, we develop an intuitive multimodal control system that leverages egocentric sensing from a wearable device. Our system integrates multiple control signals, including eye gaze, head gestures, and voice commands, to enable hands-free interaction. These signals are fused to support real-time control of both robot locomotion and arm manipulation. Experimental results show that our approach achieves performance comparable to traditional joystick-based control in terms of task completion time and user experience, while significantly improving accessibility and naturalness of interaction. Our results highlight the potential of egocentric multimodal interfaces to make mobile manipulation robots more inclusive and usable for a broader population. A demonstration of the system is available on our project webpage.

cs.RO