OMEGA LAB / RESEARCH
Research directions
We study how agents perceive, model, reason and act, from robot policies and world models to tool use, multi-agent workflows and the safety of embodied systems.
Multimodal Perception
Representing the physical world
We study 3D geometry, multimodal fusion and scene representations, combining visual, geometric and other sensory information to understand objects and their surroundings.
UniT recovers metric-scale geometry from any number of views, shown here reconstructing an indoor scene at HKUST(GZ). The project page includes more reconstructions, results and a live demo.
Explore UniTRobot Learning & Manipulation
Learning policies for action
We develop vision-language-action models that connect reasoning with manipulation, studying motion generation, policy improvement and efficient inference.
World Models & Prediction
Modeling how the world changes
We investigate predictive world models and future motion, with a focus on how behavior, driving style and physical dynamics inform planning.
Intent & Decision-Making
Understanding behavior and decisions
We model intent and human driving behavior, and examine how visual evidence and context shape autonomous decisions under real-world constraints.
Multi-Agent Systems
Coordinating agents and tools
We study how agents use specialized models and tools, share information and coordinate tasks, spanning digital workflows for analysis and generation as well as collaborative aerial and ground systems.
Embodied AI Safety
Stress-testing and hardening embodied agents
We probe how perception, planning and detection models fail under backdoor, adversarial and out-of-distribution threats, designing stealthy yet effective attacks to expose vulnerabilities and developing mitigation strategies for robust autonomous driving and multimodal systems.
Hardware
Platforms, instruments, and experimental setups.