We focus on the deep integration of robotics and AI to build next-generation robots that can learn and adapt in the real world, with key directions in robot design and control, multimodal perception, human-robot interaction, learning from video, intelligent manipulation, and embodied intelligence.
Research progress, recruiting news, and event updates from WeChat and RedNote.
Congratulations to Feng from RIL-Lab for winning first prize in the ManipDojo Bimanual Collaboration Challenge at I...
Read PostRIL-Lab won the championship at the 2026 RoboCup China competition.
04.10 2026 RedNoteBBQ
09.10 2025 RedNoteHappy Teacher's Day in 2025
06.08 2025 RedNote【双喜临门！】聂强教授结课日&入职周年庆，这波必须发小红书！😀 🌟 今日份快乐加载完毕 1⃣ 聂老师的Introduction to Robotic完美结课🎓 2⃣ 聂教授创立RIL-Lab一周年纪念日🎂 📸 今日高光时刻： • 图1：今天快乐的highlight • 图2：聂老师入职周年快乐🎉🎉 • 图3-4：第一节课与最后一节课，聂老师激情讲解的"知识高光"时刻 • 图5-9：实验室周年庆蛋糕+全员比心合照（机器人也出镜啦🤖） • 动图：聂老师经典手势"这个算法很elegant！" 💡 这一年我们get到： ✅ 凌晨2点的实验室灯光 ✅ 被聂教授"这个idea不错"夸奖时的激动 ✅ 从课堂到实验室的知识闭环 🎙 学生代表发言： "聂老师的课让我明白——好的控制算法和人生一样，都需要找到最优解✨" ❤ 特别彩蛋： 结课蛋糕上写着："Happy 1st Anniversary！——致永远在debug的我们" 聂教授发pyq夸“当老师一周年，赶上第一门课最后结课，很nice的巧合。感谢学生们对这门课的支持，也感谢实验室学生们给的满满的仪式感，只想说拥有一群宝藏学生是这段旅程莫大的开心🥳”
RIL-LAB was founded by Prof. Qiang Nie and is part of the Robotics and Autonomous Systems Thrust in the Systems Hub at HKUST(GZ). We work to tightly connect intelligence, learning, understanding, and manipulation for robots that can continuously learn and collaborate in real environments.
We aim to build a research platform with both academic depth and strong system-level implementation, emphasizing the co-development of algorithms, systems, hardware, and real-world applications. We encourage independent thinking, long-term vision, and hands-on execution.
Our research spans key components of robotic intelligence, from perception and decision-making to control, learning, and manipulation, forming a cross-disciplinary matrix for next-generation autonomous robots.
We explore structural innovation, dynamics modeling, and advanced control algorithms to build smarter, more agile, and more reliable robotic platforms.
We study unified understanding and generation across vision, language, and other sensor modalities to improve robot perception and adaptation in complex environments.
We develop manipulation methods for real-world complexity, integrating perception, decision-making, and execution.
We investigate behavior understanding, pose estimation, motion prediction, and collaborative decision-making for more natural and efficient human-robot teamwork.
Through video understanding, imitation, and skill transfer, robots learn generalizable capabilities from large-scale visual data.
We build agents with autonomous decision-making and continual improvement to enable higher-level autonomy for robots in real environments.
This section lists all public papers and research outputs from the lab, grouped by year.
ICRA 2026
ICRA 2026
IROS 2026
AAAI 2026
CVPR 2026
MICCAI 2025
ICCV 2025
IROS 2025
This video shows a dual-arm robotic system interacting with a multi-drawer storage box.
This video presents a collection of human motion examples paired with short text descriptions. A 3D human character performs various acti...
This video shows a humanoid robot interacting with a person in a laboratory setting. The robot responds to instructions by performing han...
This video presents a comparison experiment of a robotic grasping system with reflex feedback turned off and on.
This section introduces the PI and team members. The member-card area scrolls independently, and the directory entry expands more detailed profiles.
Large language models, vision-language-action models, reinforcement learning
Human-computer interaction, human motion generation and understanding
Human-Computer Interaction, Learning from Human Videos
Human-robot Interaction, Robot Control
Exoskeleton Robots, Pose Estimation
Motion control, reinforcement learning
Intelligent Manufacturing Engineering
Expand the complete directory for all 20 members, including education, profile links, and contact information.
This section shows members whose profiles are more complete.
Large language models, vision-language-action models, reinforcement learning
Artificial intelligence for biomedicine
Human-computer interaction, human motion generation and understanding
Human-Computer Interaction, Learning from Human Videos
Embodied Multimodal Large Model
Video learning, embodied world models
Human-robot Interaction, Robot Control
Latent Action Model
PhD student admitted in 2026, working on latent action models.
Exoskeleton Robots, Pose Estimation
Robotics, human pose estimation
Legged robot locomotion
Robotics and Autonomous Systems
Robotics, embedded systems
Motion control, reinforcement learning
Exoskeletons
Robotics and Autonomous Systems
Automation
Undergraduate student in Automation at Nankai University.
Intelligent Manufacturing Engineering
Undergraduate student in Intelligent Manufacturing Engineering at Xi'an Jiaotong University.
We continuously recruit PhD students, Master's students, research assistants, and interns who are passionate about robotics and AI. Here you will find rigorous mentorship, open collaboration, and impactful research problems.
The official process is now in the online application portal. You may also contact the PI by email first with your background and interests.
