Visual Intelligence

Envision seamless human-AI symbiosis from active observation and understanding of human-environment interactions, we explore the teaching of robot new skills through human demonstration. By observing human coworkers, robots acquire new skills, facilitated by multimodal sensory understanding and compositional learning. This allows robots to replicate human operations and apply learned skills in diverse contexts for seamless collaboration.

Enable machines to interpret visual data and reason about spatiotemporal relationships involving hierarchical task structures, we work on neuro-symbolic visual reasoning, unsupervised domain adaptation, and image/video generation to allow controllable instructional video creation across various domains.

Utilises AI for predictive analytics, leveraging texture and geometry from images, videos, and point clouds, we pioneer the shift towards multimodal spatial-temporal analysis, vital for visual intelligence in diverse industry domains. 

Develops real-time visual understanding and task reasoning technologies, for enhancing decision-making in wearable AR environments, and providing explainability involving complex data. We aim to augment humans in performing various tasks, utilising video streaming and neural network-based visual inference.

Focus on enhancing efficiency and safety in building and construction through the integration of computer vision, AI, robotics, and 3D spatial mapping technologies, reducing the manpower required for applications in these domains. Additionally, efforts are directed towards developing a Large Multi-Modal Model specialised in construction safety, facilitating quick identification of safety issues in videos and assisting safety inspectors in assessing work practices and environmental safety measures.