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Colab

机器人与空间系统实验室

CoLab (可乐实验室) 成立于2016年6月12日。我们在以人为中心的图像和视频理解方面拥有深厚的专业知识，核心能力涵盖人体分割、姿态估计、属性预测和重新识别。目前，我们正致力于将人工智能技术与无人机、具身智能和卫星遥感等尖端平台相结合。

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代表性工作

Towards Realistic Earth-Observation Constellation Scheduling: Benchmark and Methodology

Satellite Scheduling Benchmark

Paper Project Code

"Hi AirStar, Guide Me to the Badminton Court."

Embodied AI UAV Navigation MM2025 DEMO

Paper Project Code

UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-World UAV Imitation Learning

UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning

UAV Imitation Learning Benchmark

Paper Project Code

RoboCerebra: A Large-scale Benchmark for Long-horizon Robotic Manipulation Evaluation

Robotics Dataset Embodied AI NeurIPS 2025

Paper Project Code

Directions

Embodied AI × UAVs

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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Embodied AI × Satellites

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.

Embodied AI × Manipulation

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.

Embodied AI × Perception

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.