Robotics, Embodied AI, Navigation in vivo

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 𝐢𝐧 𝐑𝐨𝐛𝐨𝐭-𝐚𝐬𝐬𝐢𝐬𝐭𝐞𝐝 𝐒𝐮𝐫𝐠𝐞𝐫𝐲!

Thrilled to share our latest work, 𝐒𝐮𝐫𝐠𝐕𝐢𝐝𝐋𝐌, the first video-language model specifically designed to address both full and fine-grained surgical video comprehension.

Surgical scene understanding is critical for training and robotic decision-making. While current Multimodal Large Language Models (MLLMs) excel at image analysis, they often overlook the fine-grained temporal reasoning required to capture detailed task execution and specific procedural processes within a surgery. This motivated us to bridge the gap between global video understanding and micro-action analysis.

🧠✨ What we developed:

A novel framework and resource for surgical video reasoning that includes:

🔹 𝐓𝐰𝐨-𝐬𝐭𝐚𝐠𝐞 𝐒𝐭𝐚𝐠𝐞𝐅𝐨𝐜𝐮𝐬 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦: The first stage extracts global procedural context, while the second stage performs high-frequency local analysis for fine-grained task execution.

🔹 𝐌𝐮𝐥𝐭𝐢-𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐅𝐮𝐬𝐢𝐨𝐧 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 (𝐌𝐅𝐀): Effectively integrates low-frequency global features with high-frequency local details to ensure comprehensive scene perception.

🔹 𝐒𝐕𝐔-𝟑𝟏𝐊 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: We constructed a large-scale dataset with over 31,000 video-instruction pairs, featuring hierarchical knowledge representation for enhanced visual reasoning.

🎯 Key Results:

✅ SurgVidLM significantly outperforms existing models (like Qwen2-VL) in multi-grained surgical video understanding tasks.

✅ Capable of inferring anatomical landmarks (e.g., Denonvilliers’ fascia) and providing clinical motivation, moving beyond simple visual description.

✅ Demonstrated strong performance on unseen surgical tasks, proving the robustness of our hierarchical training approach.

💡 Why it matters:

This work shows that by combining global context with localized high-frequency focus, we can significantly reduce “hallucinations” in surgical AI. It provides a pathway toward more intelligent, context-aware surgical assistants that can understand not just what is happening, but how and why specific steps are performed.

🌱 What’s next?

We are exploring how to extend this multi-grained understanding to real-time intraoperative guidance and integrating it with physical robotic control for autonomous sub-tasks.

We present TMR-VLA, an end-to-end framework designed for the motion control of tri-leg silicone-based soft robots.

Miniature magnetic robots face a hardware bottleneck where the robot body is too small to integrate onboard sensors or power. This creates a gap between actuation and perception, often requiring human experts to manually adjust magnetic fields based on visual feedback. Our work aims to bridge this gap by enabling autonomous control through a multi-modal system.

🧠 Technical Framework:

●     End-to-End Mapping: The policy translates sequential endoscope images and natural language instructions directly into low-level coil voltage commands.

●     Action Adaptor: We utilized an EndoVLA-initialized backbone with an Action LoRA Adaptor that allows the model to autoregressively emit voltage increments.

●     TrilegMR-Motion Dataset: The model was trained on a new dataset containing 15,793 image-action pairs across 60 episodes.

●     Diverse Locomotion: The system controls five motion primitives: squatting, leg-lifting, rotation, forward movement, and recovery.

🎯 Experimental Results:

●     Success Rate: TMR-VLA achieved an average success rate of 74% across tested motion types.

●     Performance: The model outperformed general-purpose multimodal models (such as Qwen2.5-VL and LLaVA-1.6) in both instruction interpretation and action execution.

●     Inference Speed: Real-time control was demonstrated at approximately 2 Hz using an NVIDIA RTX 5090 GPU.

💡 Significance: This study addresses the challenge of autonomous control in untethered soft robots without increasing their structural complexity. It provides a foundational baseline for intelligent navigation in complex in-vivo environments.

Big news! 🚀 Our lab is proud to announce that 6 of our latest papers have been accepted! 🎊

We are incredibly proud of the team’s hard work and innovation. To give each project the spotlight it deserves, we will be sharing details about each breakthrough one by one over the coming days.

Stay tuned for the updates! 🤖✨

We are thrilled to share our latest Comment published in #NatureReviewsBioengineering: “𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗸𝗶𝗻𝗮𝗲𝘀𝘁𝗵𝗲𝘀𝗶𝗮 𝗶𝗻 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗿𝗼𝗯𝗼𝘁𝗶𝗰 𝘀𝘂𝗿𝗴𝗲𝗿𝘆”.

Current autonomous surgical robots are 𝗵𝗲𝗮𝘃𝗶𝗹𝘆 𝘃𝗶𝘀𝗶𝗼𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰. While they 𝗰𝗮𝗻 “𝘀𝗲𝗲” anatomy, they 𝗹𝗮𝗰𝗸 𝘁𝗵𝗲 𝗶𝗻𝘁𝗿𝗶𝗻𝘀𝗶𝗰 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 “𝗳𝗲𝗲𝗹” tissue interactions—𝗮 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 𝘀𝗸𝗶𝗹𝗹 𝘁𝗵𝗮𝘁 𝗵𝘂𝗺𝗮𝗻 𝘀𝘂𝗿𝗴𝗲𝗼𝗻𝘀 𝗿𝗲𝗹𝘆 𝗼𝗻 𝗳𝗼𝗿 𝘀𝗮𝗳𝗲𝘁𝘆 𝗮𝗻𝗱 𝗱𝗲𝘅𝘁𝗲𝗿𝗶𝘁𝘆.

In this article, we propose a hierarchical framework for 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗞𝗶𝗻𝗮𝗲𝘀𝘁𝗵𝗲𝘀𝗶𝗮 to bridge this gap:

1. 📈𝗧𝗵𝗲 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗟𝗲𝘃𝗲𝗹: Integrating proprioception and exteroception for high-res physical sensing.

2. 💬𝗧𝗵𝗲 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗟𝗲𝘃𝗲𝗹: Moving from raw signal processing to semantic understanding of contact.

3. 🧠𝗧𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗟𝗲𝘃𝗲𝗹: Implementing Vision-Kinaesthesia-Language-Action models to achieve true sensorimotor synergy.

We believe the future of autonomous surgery lies in systems that can synergistically fuse vision and kinaesthesia to not just see, but truly feel, think, and act.

📃 Read the 𝗳𝘂𝗹𝗹 𝗽𝗮𝗽𝗲𝗿 here: [https://lnkd.in/gqTEpYjs]

👏 Kudos to our amazing team, Dr. Tangyou Liu, Dr. Sishen YUAN, and Prof. Hongliang Ren.

Thrilled to share our latest The International Journal of Robotics Research (IJRR) work on enabling 𝗴𝗿𝗮𝘃𝗶𝘁𝘆‑𝗮𝘄𝗮𝗿𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 for 𝗽𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝗰𝗮𝗯𝗹𝗲‑𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝗼𝗳𝘁 𝘀𝗹𝗲𝗻𝗱𝗲𝗿 𝗿𝗼𝗯𝗼𝘁𝘀 — using 𝗼𝗻𝗹𝘆 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗜𝗠𝗨 and a powerful 𝗿𝗼𝗯𝗼𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 simulation‑driven framework.

Soft robots are lightweight and flexible, but their high aspect ratios make them 𝘦𝘹𝘵𝘳𝘦𝘮𝘦𝘭𝘺 sensitive to gravity, causing passive deformation that traditional kinematics just can’t handle. This motivated us to rethink how soft robots can 𝘴𝘦𝘯𝘴𝘦 and 𝘤𝘰𝘮𝘱𝘦𝘯𝘴𝘢𝘵𝘦 for gravity — without bulky sensors or complex hardware.

🧠✨ 𝗪𝗵𝗮𝘁 𝘄𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱:

A 𝗯𝗶𝗼‑𝗶𝗻𝘀𝗽𝗶𝗿𝗲𝗱 𝗿𝗲𝗮𝗹𝟮𝘀𝗶𝗺𝟮𝗿𝗲𝗮𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 that:

🔹 Streams real‑world IMU orientation into a real‑time and robust SOFA simulation

🔹 Dynamically reorients virtual gravity to mirror reality

🔹 Uses QP optimization to compute joint‑level compensation

🔹 Executes compensation in both simulation and the physical robot

All of this — using just 𝗼𝗻𝗲 𝗜𝗠𝗨. No strain sensors. No cameras. No expensive reconstruction systems.

🎯 𝗞𝗲𝘆 𝗥𝗲𝘀𝘂𝗹𝘁𝘀:

✅ >99% compensation recovery in static tests

✅ ~94% recovery in low‑motion dynamic tests

✅ Demonstrated on different two-segment cable‑driven soft robots

💡 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:

This work shows that soft robots can maintain 𝘀𝘁𝗮𝗯𝗹𝗲, 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝘂𝗻𝗱𝗲𝗿 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗴𝗿𝗮𝘃𝗶𝘁𝘆 by integrating 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝘀𝗲𝗻𝘀𝗶𝗻𝗴 + 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻‑𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘃𝗲𝗿𝘀𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻. It reduces reliance on physical sensors and opens a pathway toward 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘇𝗮𝗯𝗹𝗲 𝗴𝗿𝗮𝘃𝗶𝘁𝘆‑𝗮𝘄𝗮𝗿𝗲 𝘀𝗼𝗳𝘁 𝗿𝗼𝗯𝗼𝘁𝘀.

🌱 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁?

We’re exploring how to extend this architecture to virtualizable external force sensing and richer environmental interactions.

Special shoutout to the team —

Jiewen Lai, Tian-Ao Ren (co‑first authors),

Pengfei YE, Yanjun Liu, Jingyao Sun, Hongliang Ren —

for making this project possible.

🔗 Paper link: https://lnkd.in/gMgwCfvf

𝗧𝗶𝘁𝗹𝗲: 𝗔 𝗧𝗿𝗮𝗻𝘀𝗲𝗻𝗱𝗼𝘀𝗰𝗼𝗽𝗶𝗰 𝗧𝗲𝗹𝗲𝗿𝗼𝗯𝗼𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺 𝗨𝘀𝗶𝗻𝗴 𝗛𝗲𝘁𝗲𝗿𝗼𝗴𝗲𝗻𝗲𝗼𝘂𝘀 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗼𝗿𝘀 𝗳𝗼𝗿 𝗕𝗶𝗺𝗮𝗻𝘂𝗮𝗹 𝗘𝗻𝗱𝗼𝘀𝗰𝗼𝗽𝗶𝗰 𝗦𝘂𝗯𝗺𝘂𝗰𝗼𝘀𝗮𝗹 𝗗𝗶𝘀𝘀𝗲𝗰𝘁𝗶𝗼𝗻

🔍 𝗕𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱:

Endoscopic submucosal dissection (ESD) is a key technique for early GI cancer treatment, requiring high dexterity and precision.

🛠 𝗪𝗵𝗮𝘁 𝘄𝗲 𝗱𝗶𝗱:

We developed the first 𝗵𝗲𝘁𝗲𝗿𝗼𝗴𝗲𝗻𝗲𝗼𝘂𝘀 𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗼𝗿𝘀 (𝗛𝗙𝗠𝘀) for bimanual ESD, integrating:

🤖 𝗦𝗲𝗿𝗶𝗮𝗹 𝗔𝗿𝘁𝗶𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗼𝗿 (𝗦𝗔𝗠) – for stable, multidirectional tissue traction

🔬 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝘂𝗺 𝗪𝗿𝗶𝘀𝘁 (𝗣𝗖𝗪) – for accurate tissue dissection

📐 𝗞𝗲𝘆 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀:

✔ Kinematic modeling using Denavit–Hartenberg & Cosserat rod methods

✔ Workspace & dexterity analysis via simulation

✔ Validation through 16 ex vivo ESD tests

💡 This work demonstrates a novel strategy for surgical robotics—leveraging heterogeneous structures to enhance flexibility, stiffness, and accuracy in minimally invasive procedures.

👏 Kudos to our amazing team and collaborators from CUHK (Prof. Huxin Gao, Tao Zhang, Prof. Hongliang Ren), Qilu Hospital (Xiaoxiao Yang, Prof. 左秀丽, Prof. Yanqing Li), Southern University of Science and Technology (Xiao Xiao, Prof. Qinghu Meng), and Beijing Institute of Technology (Prof. Changsheng Li)!

📖 Stay tuned for the full article in IEEE RAM!

🏆 Paper: Contact-Aided Navigation of Flexible Robotic Endoscope Using Deep Reinforcement Learning in Dynamic Stomach

👩‍🔬 Authors: Chi Kit Ng, Huxin Gao, Tianao Ren, Prof. Jiewen Lai, and Prof. Hongliang Ren

🔍 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:

Navigating flexible robotic endoscopes in the dynamic, deformable stomach environment is a grand challenge. Our proposed Contact-Aided Navigation (CAN) strategy, powered by deep reinforcement learning and force-feedback, achieved:

• 100% success rate in both static and dynamic simulated stomach environments

• Average navigation error of just 𝟭.𝟲 𝗺𝗺

• Robust generalization even under strong external disturbances

This work highlights how 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗔𝗜 𝗮𝗻𝗱 𝗯𝗶𝗼𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀-𝗶𝗻𝘀𝗽𝗶𝗿𝗲𝗱 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 can transform surgical robotics, enabling safer and more precise navigation in complex clinical environments.

Check the paper at https://lnkd.in/g6KgZTdD

🙏 Huge thanks to the team, collaborators, and the broader robotics community for the support and inspiration.

This work introduces a comprehensive dataset designed to advance AI-driven surgical robotics and medical imaging. By capturing detailed 𝗮𝗻𝗮𝘁𝗼𝗺𝗶𝗰𝗮𝗹 𝗹𝗮𝗻𝗱𝗺𝗮𝗿𝗸𝘀 𝗼𝗳 𝘁𝗵𝗲 𝘂𝗽𝗽𝗲𝗿 𝗮𝗶𝗿𝘄𝗮𝘆, we aim to support safer, more accurate 𝗯𝗿𝗼𝗻𝗰𝗵𝗼𝘀𝗰𝗼𝗽𝘆 𝗮𝗻𝗱 𝗶𝗻𝘁𝘂𝗯𝗮𝘁𝗶𝗼𝗻 procedures — paving the way for improved patient outcomes and robust benchmarking in clinical AI.

🔑 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:

– First-of-its-kind dataset focused on airway anatomical landmarks

– Enables benchmarking for automated navigation and intubation tasks

– Openly available to foster collaboration across robotics, AI, and healthcare communities

We hope this resource will accelerate innovation in 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 and inspire new interdisciplinary collaborations.

👉 Read the full paper: https://rdcu.be/eS0d5

Grateful to all co-authors and collaborators from The Chinese University of Hong Kong (Ruoyi Hao, Zhiqing Tang, Catherine Po Ling Chan, Jason Ying Kuen Chan, Prof. Hongliang Ren), Hubei University of Technology (Zhang Yang), Huazhong University of Science and Technology (Yang Zhou), National University of Singapore (Lalithkumar Seenivasan), and Singapore General Hospital (Shuhui 

Xu, Neville Wei Yang Teo, Kaijun Tay, Vanessa Yee Jueen Tan, Jiun Fong Thong, Kimberley Liqin Kiong, Shaun Loh, Song Tar Toh, and Prof. Chwee Ming Lim), for making this possible. Excited to see how others build upon this foundation!

💡This work introduces 𝗩𝗟-𝗦𝘂𝗿𝗴𝗣𝗧, the first large-scale multimodal dataset that 𝘪𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘦𝘴 𝘷𝘪𝘴𝘶𝘢𝘭 𝘵𝘳𝘢𝘫𝘦𝘤𝘵𝘰𝘳𝘪𝘦𝘴 𝘸𝘪𝘵𝘩 𝘴𝘦𝘮𝘢𝘯𝘵𝘪𝘤 𝘱𝘰𝘪𝘯𝘵 𝘴𝘵𝘢𝘵𝘶𝘴 𝘥𝘦𝘴𝘤𝘳𝘪𝘱𝘵𝘪𝘰𝘯𝘴 in surgical environments.

🔍Alongside the dataset, we propose 𝗧𝗚-𝗦𝘂𝗿𝗴𝗣𝗧, a text-guided point tracking method that consistently outperforms vision-only approaches, especially under challenging intraoperative conditions such as smoke, occlusion, and tissue deformation.

🙏 We are deeply grateful to all coauthors and especially our clinical collaborators at Shenzhen People’s Hospital for their invaluable contributions. Looking forward to engaging with the community at AAAI in Singapore and advancing the conversation on multimodal surgical AI!

Check the paper at https://lnkd.in/grfE5iVi

Project Page: https://lnkd.in/gscM_ciV

🎉 Excited to share that the 3rd C4SR+ Workshop: 𝘾𝙤𝙣𝙩𝙞𝙣𝙪𝙪𝙢, 𝘾𝙤𝙢𝙥𝙡𝙞𝙖𝙣𝙩, 𝘾𝙤𝙤𝙥𝙚𝙧𝙖𝙩𝙞𝙫𝙚, 𝘾𝙤𝙜𝙣𝙞𝙩𝙞𝙫𝙚 𝙎𝙪𝙧𝙜𝙞𝙘𝙖𝙡 𝙍𝙤𝙗𝙤𝙩𝙞𝙘 𝙎𝙮𝙨𝙩𝙚𝙢𝙨 𝙞𝙣 𝙩𝙝𝙚 𝙀𝙢𝙗𝙤𝙙𝙞𝙚𝙙 𝘼𝙄 𝙀𝙧𝙖 took place during #IROS2025 in Hangzhou, China.

This year’s workshop attracted 𝗼𝘃𝗲𝗿 𝗼𝗻𝗲 𝗵𝘂𝗻𝗱𝗿𝗲𝗱 𝗽𝗮𝗿𝘁𝗶𝗰𝗶𝗽𝗮𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗴𝗹𝗼𝗯𝗲 — a fantastic turnout that reflects the growing momentum in 𝘀𝘂𝗿𝗴𝗶𝗰𝗮𝗹 𝗿𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗲𝗺𝗯𝗼𝗱𝗶𝗲𝗱 𝗔𝗜.

🎤 Distinguished Speakers

We were honored to host leading experts who shared their groundbreaking research and perspectives, including Prof. Nassir Navab from Technical University of Munich, Prof. Leonardo Mattos from Italian Institute of Technology, Prof. Mingchuan Zhou from Zhejiang University, Prof. Dandan Zhang from Imperial College London, Prof. Yunjie Yang from University of Edinburgh, and Prof. Guoying Gu from Shanghai Jiao Tong University.

🔑 Key Themes Discussed

• Embodied AI in surgery and intelligent operating rooms

• Soft & continuum robotics for minimally invasive procedures

• Human–robot collaboration in clinical practice

• Cognitive surgical systems and decision-making

🏆 Workshop Contributions

– 9 oral and 9 poster paper presentations from emerging researchers

– Best Paper & Best Presentation Awards recognizing outstanding contributions

🙏 A heartfelt thank you to all speakers, participants, and organizers who made this workshop such a success. The discussions and collaborations will continue to shape the future of surgical robotics.

🔗 Learn more about the workshop and its highlights on the official page: https://lnkd.in/gswzMFAy

The work couples a VLM backbone with a dual-phase training recipe (SFT → RFT) to turn language prompts into robust tracking and motor commands for a continuum endoscope.

A highlight was a meeting with Prof. Ken Goldberg—especially meaningful since Prof. Ren previously served as a postdoc at UC Berkeley, keeping our CUHK↔︎UCB connection strong.

🤝 Huge thanks to collaborators and everyone who stopped by the poster!

Authors: Chi Kit Ng*, Long Bai*, Guankun Wang*, Yupeng Wang, Huxin Gao, Kun Yuan, Chenhan Jin, Tieyong Zeng, Hongliang Ren
Affiliations: CUHK, TUM