Tin Lun LAM

PhD (CUHK), IEEE Senior Member

Assistant Professor - Presidential Young Fellow, School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen

Executive Deputy Director, the National-local Joint Engineering Laboratory of Robotics and Intelligent Manufacturing

Director, Center for Intelligent Robots, Shenzhen Institute of Artificial Intelligence and Robotics for Society

Vice Chair, IEEE Robotics and Automation Society Guangzhou Chapter

tllam@cuhk.edu.cn | tinlun.lam@ieee.org | Linkedin | Google Scholar | ORCID

Tin Lun LAM, a Senior Member of IEEE, is currently an Assistant Professor - Presidential Young Fellow at The Chinese University of Hong Kong, Shenzhen. He is also an Executive Deputy Director of the National-local Joint Engineering Laboratory of Robotics and Intelligent Manufacturing and a Center for Intelligent Robots Director at Shenzhen Institute of Artificial Intelligence and Robotics for Society. Additionally, he serves as an Associate Editor for the Journal of Field Robotics and ICRA. He received his B.Eng. Degree with First Class Honors and Ph.D. Degree in Robotics and Automation from The Chinese University of Hong Kong in 2006 and 2010, respectively. His research focus includes Field Robotics and Multi-robot Collaboration. He has authored and co-authored 3 books and published more than 70 research papers in top-tier international journals and conference proceedings in Robotics and AI (Nature Communications, T-RO, IJRR, TPAMI, TIP, JFR, TMECH, RA-L, ICRA, IROS, etc.). He has been granted more than 80 patents. He received the IEEE/ASME TMECH Best Paper Award in 2011, the IEEE/RSJ IROS Best Paper Award on Robot Mechanisms and Design and Best Application Paper Award Finalist in 2020, and the Distinguished Young Scholars Fund from Guangdong Natural Science Foundation in 2023. His research outcomes were reported in many internationally renowned media, including Reuters, Forbes, Discovery, IEEE Spectrum, CCTV, NIKKEI, and NHK.

Research Interests

Field Robotics, Modular Robots, Multi-robot Collaboration

Freeform Robotics Research Group: freeformrobotics.org

Selected Projects

Freeform Robot

Modular self-reconfigurable robotic systems have strong self-adaptation and self-healing abilities, which makes them capable of coping with various tasks in complex and changeable environments. Most existing modular self-reconfigurable robots have connection constraints on the mechanical structure. Their relative positioning and motion planning capacities are also limited to the structured environment, which is different from the real and changing environment. To break these limitations, this project aims to develop key technologies for modular self-reconfiguration robots to be applied in unstructured environments. The research results can lay a theoretical and technical foundation for the development of swarm robots and field robots, and can also be widely used in the areas of search and rescue, space exploration, etc. 

[2020 IROS Best Paper Award on Robot Mechanisms and Design] 

Metal Spheres Swarm Together to Create Freeform Modular Robots - IEEE Spectrum 

Magnetic FreeBOT orbs work together to climb large obstacles | Engadget 

《超能陆战队》磁性球体机器人成真?中国内地高校近十年来首获机器人顶会IROS最佳论文奖 

现实版《超能陆战队》:港中深「新型自重构机器人」获IROS最佳机器人设计提名

《超能陆战队》成真?香港中文大学(深圳)自由重构机器人再升级


Collaborative Loco-manipulation

The collaboration between the University of Edinburgh (UoE) and Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) aims at fundamental and applied researches in artificial intelligence and robotics. The current research focuses on three scientific pillars: Multi-Contact Planning and Control, Multi-Agent Collaborative Manipulation, and Robot Perception.

PI: Prof. Sethu Vijayakumar (UoE), Prof. Tin Lun Lam (AIRS)

More information: https://airs.cuhk.edu.cn/en/page/357

http://web.inf.ed.ac.uk/slmc/research/projects-and-grants/uoe-airs-joint-project 

Whole-Body Control for Velocity-Controlled Mobile Collaborative Robots - YouTube

Fast and Comfortable Interactive Robot-to-Human Object Handover - YouTube


Heterogeneous Multi-robot Planning

A heterogeneous multi-robot cooperative system comprises a team of robots with different configurations that can cooperate to achieve complex tasks in dynamic environments. We study the problems of accurate modeling, real-time solving, and dynamic adaptation for the collaborative planning problem of heterogeneous multi-robot systems. By constructing a constrained multi-task assignment and scheduling model, we use operations research algorithms and machine learning techniques to solve and analyze specific scenario tasks. The research results provide theoretical support for using robots in complex and changing environments in reality. Future application scenarios include unmanned mines, security, anti-terrorism, dynamic path planning, human-machine co-adaptation, etc.

Intelligent Heterogeneous Multi-robot Catching System using Deep Multi-agent Reinforcement Learning - Youtube 

Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks - Youtube

Multi-robot  Environment Perception 

In multi-robot systems, environment perception and data fusion from different sources are crucial for effective collaboration. This research aims to address the challenges associated with data matching and visual interference in such systems, focusing on three main issues:

1. Data Matching Difficulty due to Time Differences: The difference in data collection times can lead to variations in lighting conditions, making it challenging to match and fuse data from multiple sources.

2. Data Matching Difficulty due to Viewpoint Differences: The different perspectives from which robots collect data can cause inconsistencies, making it difficult to match and integrate the information for a comprehensive understanding of the environment.

3. Visual Interference among Robots in the Same Environment: As multiple robots move and operate within the same environment, they may generate visual interference with each other, further complicating data matching and fusion processes.

This study aims to propose solutions to overcome these challenges and improve the overall performance of multi-robot collaborative systems in complex environments.

A Two-stage Unsupervised Approach for Low Light Image Enhancement - YouTube

Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization - YouTube

RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic Objects - YouTube

Inflatable Robotic Arm and Finger 

In this project, the mechanical design and implementation of a low-cost and lightweight inflatable robotic arm and finger are proposed. The proposed inflatable arm and finger use a common and low-cost inflatable material and can be easily and massively manufactured. The inflatable arm can work by pumping air at very low pressure and allows direct and soft human contact without any external force sensors.

Design, Kinematics, and Control of a Multijoint Soft Inflatable Arm for Human-Safe Interaction - IEEE Journals & Magazine 

Mechanical design and implementation of a soft inflatable robot arm for safe human-robot interaction - IEEE Conference Publication 

Design and implementation of a low-cost and lightweight inflatable robot finger - IEEE Conference Publication 

Tree-climbing Robot

Treebot is a tree-climbing robot that has high maneuverability in an irregular tree environment. It is able to reach many places on trees including branches that surpass the state-of-the-art tree-climbing robots. Treebot can maneuver in a complex tree environment, but only five actuators are used in the mechanism. As a result, Treebot can keep it in a compact size and lightweight.

Treebot Learns to Autonomously Climb Trees | IEEE Spectrum

Meet Treebot, the tree-climbing forest sentinel | Reuters 

Tree Climbing Robot - Design, Kinematics and Motion Planning | Tin Lun Lam | Springer 

Climbing Strategy for a Flexible Tree Climbing Robot—Treebot - IEEE Journals & Magazine 

A flexible tree climbing robot: Treebot - design and implementation - YouTube 

Omni-directional Vehicle 

A novel omnidirectional steer-by-wire system is proposed for omni-directional vehicles. The system includes an extended steering interface and a behavior-based steering controller. The extended steering interface provides a novel manipulation way for the vehicle driver. The driver can control the vehicle in the traditional way or omnidirectionally without any mode switching operation. The reservation of the traditional driving way makes the driver adapt to the novel steering interface easily. 

[2011 IEEE/ASME Transactions on Mechatronics Best Paper Award]

Asia Tech Week - Omni-Directional Car | Discovery 

McGraw Hill Canada | Hybrid Electric Vehicle Design and Control: Intelligent Omnidirectional Hybrids 

Omnidirectional Steering Interface and Control for a Four-Wheel Independent Steering Vehicle - IEEE Journals & Magazine 

Autonomous Sailboat 

Ocean exploration has attracted enormous interest from humankind for thousands of years. This project focus on developing energy sustainable robot for ocean exploration. One of the solutions is harvesting wind power for a vehicle's propulsion by sail. This project presents a new autonomous surface vehicle (ASV) based on retrofitting a trimaran sailing boat. The sail and rudder control are motorized by three electric actuators. The ASV acquires signals of wind speed and direction, GPS position, and rolling angle, and determines the heading based on wind direction.  

[2020 IROS Best Application Paper Award Finalist] 

OceanVoy- A Hybrid Energy Planning System for Autonomous Sailboat - YouTube

Selected Publications

Julien Bourgeois, Jamie Paik, Benoît Piranda, Justin Werfel, Sabine Hauert, Alyssa Pierson, Heiko Hamann, Tin Lun Lam, Fumitoshi Matsuno, Negar Mehr, Abdallah Makhoul

Gang Peng, Tin Lun Lam, Chunxu Hu, Yu Yao, Jintao Liu, Fan Yang

Yangsheng Xu, Jingyu Yan, Huihuan Qian, and Tin Lun Lam 

Tree Climbing Robots: Design, Kinematics and Motion Planning

Springer Tracts in Advanced Robotics, 2012

Tin Lun Lam, and Yangsheng Xu 

Books:

Journals: (*Corresponding author) 

Conferences: (*Corresponding author)