1. Our lab members Sishen YUAN, Tao Zhang, YIMING HUANG, and Yupeng Wang presented 6 papers covering medical robotics, augmented reality, and surgical scene rendering, among others.
2. One of our accepted papers, entitled “Multimodal Augmented Reality Assisted Incision Guidance for Preoperative Tracheostomy Planning” was awarded the Finalist of Best Student Paper Award!
3. Prof. Hongliang Ren gave a talk on “Compliant Endoscopic Motion Generation and Perception towards Intelligent Minimally Invasive Robotic Procedures” at the Workshop on Actuation, Control, Fabrication, and Application of Miniature Robots in Biomedical Engineering.
We are grateful for the opportunity to share our work and look forward to continuing our research in medical robotics and AI.
During the 2024 International Automatic Control Conference (CACS 2024, https://lnkd.in/gYVg4tx5), Prof. Hongliang Ren was invited as a keynote speaker and delivered a talk on “Compliant Robotic Motion Control and Perception Towards Intelligent Minimally Invasive Surgical Procedures”. The conference provided a great platform for sharing our latest research and connecting with fellow experts in the field.
These works are based on strong collaboration between LabREN (http://www.labren.org/mm/) and ABILab (https://lnkd.in/gUuzQqDt). We are looking forward to continuing our collaborative efforts in these areas.
A particular highlight was our effort towards automated and intelligent Endoscopic Submucosal Dissection (ESD) surgery, empowered by our indigenous DREAMS (Dual-arm Robotic Endoscopic Assistant for Minimally Invasive Surgery, https://lnkd.in/gQ3PcMsn) platform. We demonstrated how this platform can enhance ESD procedures when equipped with advanced features such as precise trajectory planning, intelligent cutting decision support, accurate reconstruction, and granular analysis and prediction of motion (https://lnkd.in/gkF6A4QY) empowered by Large Visual-Language Models (LVLM).
In addition to our progress in ESD, we also presented other projects, including surgical scene reconstruction & depth estimation (https://lnkd.in/gviyHxAp, https://lnkd.in/gtDwbyWg), augmented reality applications in surgery, the skull-mounted neuro-interventional robot (SkullBot, https://lnkd.in/gn8N9zdU), and OCT for port-wine stain analysis (https://lnkd.in/gm6iH3-m).
This visit not only provided an opportunity for knowledge exchange and collaboration but also reaffirmed our lab’s commitment to pushing the boundaries of innovation in the fields of medical robotics and intelligent surgical technologies. We look forward to furthering these conversations and forging new partnerships that will drive the future of healthcare forward.
On the Info Day for Undergraduate Admissions 2024, we were thrilled to see a big crowd of visitors joining the admission talk and special event โ๐๐๐ฅ๐ฅ-๐๐จ๐ฎ๐ง๐๐๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ ๐๐ง๐๐ฎ๐๐๐ญ๐ข๐จ๐ง ๐๐ก๐จ๐ฐ๐ซ๐จ๐จ๐ฆโ to learn the experience in developing APP / Products / Services.
Our lab member, Sishen YUAN, along with others, showcased our progress in augmented reality for neuro-interventional head-mounted robotics (SkullBot), attracting a lot of interest from the visitors and sparking engaging conversations about the future of surgical technology.
Thank you to everyone who joined us and showed interest in our work. We look forward to more opportunities to share our passion and advancements with the surgical robotics community!
We are thrilled to share a new research paper that’s just been published in *Communications Engineering*, showcasing a remarkable advancement in the field of origami-inspired tiny robots. ๐ฐโจ
**๐ [Untethered Bistable Origami Crawler for Confined Applications](https://lnkd.in/gpJHEM-q)
### What’s the Buzz About?
This research introduces a **magnetically actuated bistable origami crawler**, a miniature robot designed to navigate and perform tasks in confined spaces which are challenging for traditional tethered or wired devices. ๐
### Key Highlights:
– **Shape-Morphing Capability**: The crawler can transform between an undeployed locomotion state and a deployed load-bearing state, thanks to its bistable design. ๐ง
– **Robust Locomotion**: Utilizes out-of-plane crawling for bi-directional locomotion and navigation, exhibiting robust navigation even in high-friction environments. ๐ค๏ธ
– **Load-Bearing Applications**: The deployed state allows the crawler to execute tasks like microneedle insertion, opening up possibilities for medical interventions. ๐ฉบ
– **Untethered Operation**: Equipped with internal permanent magnets, this crawler operates without the need for external tethers, enhancing its maneuverability and miniaturizability. ๐ช
### Why It Matters:
This technology could provide an alternative approach to solve problems encountered in confined environments, from medical procedures in the gastrointestinal tract to complex engineering tasks in tight spots. ๐
### What’s Next:
The concept proposed in this work can also be adapted and applied to a variety of other deployable and load-bearing applications, such as fluid collection, stents, and airway support mechanisms. The proposed mechanism design can also be potentially integrated with other actuation methods like pneumatic systems for larger scale applications. ๐
We’re eager to hear your thoughts on what we hope is an innovative research! How do you envision this technology being used in your field? Share your ideas and let’s discuss the future of robotics and origami engineering! ๐ค๐
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*Don’t forget to check out the full paper for a deep dive into the mechanics, applications, and implications of this incredible new technology. It’s a must-read for anyone interested in the cutting edge of robotics and engineering innovation!* ๐๐ก
Our paper entitled “LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion”, led by our former lab member Tong Chen, Qingcheng Lyu, and our PhD student Long Bai, has been awarded the ๐MICCAI 2024 Best Paper Runner-Up!๐
This is a collaborative work between the lab of Prof Luping Zhou from The University of Sydney, our #LabREN (http://www.labren.org/mm/), and Qilu Hospital of Shandong University. Congrats to all coauthors!!!
We have also ranked #2nd in the very competitive BraTS Challenge on Sub-Sahara-Africa Adult Glioma (BraTS-SSA)! Our methodology is so-called “Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis”. Stay tuned for more updates!
Congrats to Team members: Yanguang Zhao, Long Bai, Zhaoxi Zhang, and Prof Hongliang Ren from #LabREN at The Chinese University of Hong Kong, Dr. Yanan Wu from China Medical University, and Dr. Mobarakol Islam from University College London.
Robotic endoscopes play a crucial role in diagnosing gastrointestinal disease and performing tumor resections. While current research primarily focuses on autonomously controlling rigid robots, establishing control models for flexible robots remains challenging. To address this, model-free deep reinforcement learning (DRL) presents a promising approach for enabling agents to make decisions under uncertainty.
In this paper, we investigate the control policy of a flexible endoscope using Simulation Open Framework Architecture (SOFA) platform. We design a flexible tendon-driven robotic endoscope (TDRE) and develop a custom simulation environment within SOFA to train DRL agents. Our approach involves implementing the Proximal Policy Optimization (PPO) algorithm to approximate an optimal policy for trajectory planning. The optimal policy facilitates trajectory tracking tasks for the TDREโs end-effector, such as circle trajectories and action disturbances, without requiring fine-tuning policy network parameters. Experimental results demonstrate that our approach achieves near real-time performance (30 FPS). The feedforward neural network of the policy provides feedback, enabling closed-loop control of TDRE. Furthermore, our experiments show that the navigation success rate of TDRE exceeds 90% within a tolerant error of 3 mm in free space. Notably, compared to direct training with contact, navigation tasks with contact retrained by a pre-trained policy in free space exhibit enhanced navigation capabilities.
