CoLab (Cola Laboratory, 可乐实验室) was founded on June 12, 2016. Our current research focuses on embodied intelligence in real-world environments, with emphasis on UAV, VLA, and multimodal video understanding. We also study 3D perception for autonomous agents and intelligent scheduling for satellite constellations, building AI agents that can perceive, reason, and act across air-space-ground scenarios.
UAVClaw brings OpenClaw task planning to real-world UAV autonomy, enabling drones to understand goals and execute tasks in open environments.
We build aerial embodied agents that integrate vision-language understanding with autonomous policy planning, enabling UAVs to perceive, think, and act in dynamic environments. Our research spans from simulation to real-world deployment, advancing aerial robots capable of intelligent navigation and multi-agent collaboration.
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Launching satellite constellations into orbit is hard, but efficiently scheduling them to execute tasks is equally challenging. As constellations scale up, manual planning can no longer keep pace with mission demands. We develop AI-driven scheduling and planning methods for large-scale satellite constellations, tackling the NP-hard challenges of dynamic Earth observation task allocation under tight time windows and complex physical constraints.
Our manipulation research explores learning-based methods for robotic grasping, dexterous hand control, and human-object interaction. We develop policies that enable robots to manipulate diverse objects and perform complex tasks in unstructured environments.
Beyond simple "seeing", we develop agents that deeply understand environments across spatial, temporal, and semantic dimensions. Key tasks include open-vocabulary detection and segmentation, enabling models to recognize and reason about novel objects.
