Yanliang Zhang

🚀 Excited to share my learnings in Isaac Sim! 🚀 Isaac Sim is a high-fidelity simulation software with incredible use cases, especially in reinforcement learning. One challenge I encountered was successfully importing robot models due to two key reasons: 1️⃣ Isaac Sim only accepts URDF files. 2️⃣ Many GitHub packages for robot models are defined as Xacro files with complex layering. After some investigation, I found a solution that I believe will be helpful for anyone getting started and trying to import their robot model. I used the Xarm 5 as an example, and I’ve documented the process in a YouTube video: https://lnkd.in/gh-D_JK8. This approach can be adapted for any robot model. Happy learning with Isaac Sim! 🎉 #robotics #simulation

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Guan Hong Tan when you and I worked on ST Engineering Autonomous full-sized Bus, then I was co-Investigator from National University of Singapore NUS Advanced Robotics Centre ARC, you were leading ST Engineering Program, since 2017. We saw many challenges, Technology limitations, shortage of Talents, too slow to grooming them up to speed, lack of resources availability, low budget allocation, too ambitious from Land Transport Authority LTA at that early stage to do a 12-metres Bus when even a small car yet to go full Autonomy. * We learnt a great deal then until yesterday. Unfortunately, you have embraced that past and measure what Autonomous Technology state-of-the-art of advancing it's improvement and make yourself Singapore's number one Naysayer that the hidden danger of Autonomous Vehicles will put impossibility on Singapore roads. Just a week ago, human-driven cars pile-up on top of each other along ECP towards Changi Airport. Luckily, no death, but to show to the World how dangerous Singapore roads have become. Your hypocritical judgment that Autonomous Vehicles cannot make it in Singapore strikes that fundamental belief that Computers cannot drive as good as Humans? Also to finger at Artificial Intelligence inability to do this job, regardless of progress in Machine Intelligence. I view your assessment as Cynicism. For Innovation to happen, Technology has many ways to enter the marketplace. While Autonomous Vehicle Startups chose full Autonomy as the target productization, like Tesla; Toyota took the incremental approach with Assistive driving, in small steps. My family's Toyota Camry Hybrid has Autonomous braking, lane-keeping and Radar Cruise speed and distance control, in addition to Pedestrian detection at crossings. By demonizing Autonomous Vehicles, instead of coming out of your retirement to improve Autonomous Technology, you will wipe-out your previous good work at ST Engineering Autonomous Team - You can leave your Legacy in Singapore, unfortunately you now choose to put it into History. I urge you to be proactive to Seeing What's Next, the things of positive, while Autonomous Technology has its infancy naive nascent beginning, let's take this progressively as Sustaining Innovation. Give it time, more lessons well learnt, for Advancement of Science, Technology, Engineering and Mathematics STEM field; most importantly, Economic Development and Growth for our next-generation Singaporeans into New Jobs creation. My advice to you, while I am just another very ordinary Singapore-born 55-years old Singaporean Scientist Engineer in Singapore; essentially, a Nobody, but ..... Leadership Initiative with Hope, not Cynicism and Naysayer to bury our Youth, rather we shall Excite and Inspire What's Possible, than you keep saying Impossible. ** My name is William Lee, I believe Autonomous Technology, Robotics AI and Machine Intelligence will be the New Industrialization era for the World, I encourage our Kids to Go FutureReady.

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Researchers at Tohoku University and the Japan Atomic Energy Agency have developed fundamental experiments and theories to manipulate the geometry of the ‘electron universe,’ which describes the structure of electronic quantum states in a manner mathematically similar to the actual universe, within a magnetic material under ambient conditions. The investigated geometric property – i.e., the quantum metric – was detected as an electric signal distinct from ordinary electrical conduction. This breakthrough reveals the fundamental quantum science of electrons and paves the way for designing innovative spintronic devices utilizing the unconventional conduction emerging from the quantum metric. Electric conduction, which is crucial for many devices, follows Ohm’s law: a current responds proportionally to applied voltage. But to realize new devices, scientists have had to find a means to go beyond this law. Here is where quantum mechanics come in. A unique quantum geometry known as the quantum metric can generate non-Ohmic conduction. This quantum metric is a property inherent to the material itself, suggesting that it’s a fundamental characteristic of the material’s quantum structure. Principal investigator Shunsuke Fukami further added, “Until now, the quantum metric has been believed to be inherent and uncontrollable, much like the universe, but we now need to change this perception. Our findings, particularly the flexible control at room temperature, may offer new opportunities to develop functional devices such as rectifiers and detectors in the future.” #ohm #quantum #physics #conductivity #spintronics #electron #universe https://lnkd.in/gv22zNmx

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We got the FA Foundation Paper Award for our joint paper with Waseda University's Ogata Laboratory! (This paper also won the Advanced Robotics Best Paper Award in this summer.) FA財団論文賞を受賞しました!! 早稲田大学尾形研との共同論文です。 (夏にAdvanced Robotics Best Paper Awardも受賞した論文です。) Paper title: "Learning-based collision-free planning on arbitrary optimization criteria in the latent space through cGANs" （Tomoki Ando, Hiroto Iino, Hiroki Mori, Ryota Torishima, Kuniyuki Takahashi, Shoichiro Yamaguchi, Daisuke Okanohara, Tetsuya Ogata） https://lnkd.in/dQK4_rN9 https://lnkd.in/dUaUNB7j Short summary: In robot arm path planning, our method learns collision-free robot arm poses in the latent space through cGANs. By connecting start and end points with arbitrary line segments in this space, it generates various obstacle-avoiding trajectories 概要：ロボットアームのpath planningで、障害物と衝突しないロボットアームの姿勢をGANの潜在空間に獲得させ、この空間上で始点と終点を任意の線分で結ぶと、障害物回避する様々な軌道を生成します。

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Special Issue: Designing Intelligent Man-Machine Interfaces for Enhanced User Interaction ( JISIoT) Designing Intelligent Man-Machine Interfaces for Enhanced User Interaction Intelligent Man-Machine Interfaces (IMMI) is to revolutionize human interaction with technology by utilizing advanced technologies such as artificial intelligence, machine learning, and natural language processing to create intuitive and spontaneous user experiences. The scope of IMMI includes enhancing the efficiency and authenticity of these interactions, improving customer satisfaction through individualized solutions based on user behavior and preferences, and making technology more accessible to a wider audience, including individuals with disabilities. IMMI also aims to boost productivity and efficiency in professional settings by streamlining workflows, automating routine tasks, and enabling quicker decision-making processes. As AI and machine learning systems learn from user interactions, they progressively enhance the relevance and accuracy of their responses, while multimodal interactions—combining speech, touch, gesture, and facial recognition—offer a versatile and robust user experience. The development and testing of these interfaces rely on tools like Sketch, Figma, Adobe XD, and AI frameworks such as TensorFlow, PyTorch, and Keras. IMMI also integrates speech recognition and NLP capabilities through tools like Google Cloud Speech-to-Text, Amazon Lex, and IBM Watson, and leverages AR and VR technologies to create immersive experiences in education, gaming, and healthcare. As AI continues to advance, IMMI is expected to become more prevalent in everyday electronics, enabling sophisticated customization and improved accessibility for all. However, the increasing use of AI and data collection within IMMI presents significant privacy concerns, requiring robust data security measures and transparent privacy policies. Technological constraints and user acceptance challenges also highlight the need for ongoing research, development, and effective communication of IMMI's benefits. Despite these challenges, IMMI remains focused on enhancing user experiences, increasing accessibility, and driving productivity across various domains. List of topics include but not limited to: • Role of Intelligent Man-Machine Interfaces for enhanced user interaction. • Utilization of AI and ML for enhanced user experiences in IMMI. • Analysis of IMMI and Accessibility for disabled users. • Optimization of privacy and security considerations in Intelligent Man-Machine Interfaces. • Future trends and emerging technologies of IMMI. • Contribution of emotion recognition in enhancing IMMI interactions. Submission deadline: January 25, 2025 Author notification: March 25, 2025 Revised papers due: May 30, 2025 Final notification: July 30, 2025 Full details found here: https://lnkd.in/dMRRdNEr

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🧲 🧠 "Magnetic actuation for mechanomedicine" just published in Wiley Advanced Intelligent Systems! 🌍 This perspective is the result from a very estimulating collaboration between Boston (Ritu Raman, Massachusetts Institute of Technology), Zurich (Simone Schuerle, ETH Zürich), Singapore ( Andy Kah Ping Tay, National University of Singapore) and Madrid (Universidad Carlos III de Madrid). 💉 We provide a perspective on the use of magneto-responsive materials for mechanobiology and mechanomedicine. https://lnkd.in/dHFbfr4C

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Must seen for who’s interested in recent advances in robot learning for manipulation.

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If you are interested in HPC+Robotics, please check the pre-print of our upcoming paper at ICS'24 at Kyoto, Japan: https://lnkd.in/gAq9G_kq This paper is a product of the collaboration with my roboticist colleague, Neil Dantam. We proposed a methodology for cyber-physical systems to consider a large variety of real-world constraints during task scheduling. #CPS #HPC #robotics #autonomous

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https://lnkd.in/g9xx7Ztd Unification of all fundamental forces using Alena Tensor In this work we established new framework to include gravity into QFT by doing it in flat curvature but introducing interaction with gravity using Alena tensor instead which will be proved to be equivalent to contracted christofel symbol as we have proposed before. The Unified Theory That All Seek The universe hides its forces in its threads, Gravity, immense, its path widespread. Electromagnetic fields entwined, Nuclear siblings with power combined. Scientists dream deep in their hearts, To unite all forces, their paths chart. Alena Tensor, a bridge to start, A journey to unify every part. In smooth and curved simplicity, The Christoffel symbols hold the key. Each sign guides the light to see, A theory for the universe, in harmony. All four forces join as one, Bound together, under the sun. In this theory so brightly spun, The truth will shine when the work is done! Die Vereinheitlichte Theorie, die alle suchen Das Universum verbirgt seine Kräfte im Band, Die Schwerkraft ist groß und mächtig bekannt. Elektromagnetische Felder verwoben klar, Kernkräfte vereint, so wunderbar. Die Wissenschaft träumt tief und rein, Alle Kräfte zu einen, das Ziel muss sein. Alena Tensor, der Brücke Schein, Ein Pfad zur Einheit, so licht und fein. In glatter und gebogener Symmetrie, Zeigen Christoffel-Symbole die Melodie. Jedes Zeichen führt das Licht zur Theorie, Ein Universum in perfekter Harmonie. Alle vier Kräfte in einem vereint, Zusammengeführt, wie der Himmel scheint. In dieser Theorie, die so hell erscheint, Wird die Wahrheit leuchten, wenn das Werk gemeint!

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We are running the next ASTRA-sim tutorial at MICRO 2024! In this tutorial we will introduce MLCommons Chakra - a distributed ML workload schema that we have been collaboratively developing since the last 1+ year. We will present mechanisms to collect execution traces from real multi-node AI clusters and feed them to ASTRA-sim to study the impact of next generation compute and/or network fabrics. We will also introduce recent updates to ASTRA-sim: including (1) Chakra-ASTRA-sim interface, (2) the ability to evaluate *custom* collective algorithms on diverse network fabrics, and (3) study LLM-specific optimizations. Resources: - Tutorial Website: https://lnkd.in/eGy9Gs-z - ASTRA-sim: https://lnkd.in/eX-2KzJN - Chakra: https://lnkd.in/e968Xsga Presenters: Tushar Krishna (GT) William Won (GT) Joongun Park (GT) Taekyung Heo (NVIDIA) Vinay Ramakrishnaiah (AMD)

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Our paper, "Inverse Design of Snap-Actuated Jumping Robots Powered by Mechanics-Aided Machine Learning", has been published in IEEE Robotics and Automation Letters. Here, we propose a physics-data hybrid inverse design strategy to endow the snap-jump robot with diverse jumping capabilities. We first develop a discrete numerical framework for the dynamic analysis of snap-actuated jumping robots and then use extensive simulation data to establish a data-driven inverse design solution. This approach allows rapid exploration of parameter spaces to achieve targeted jump trajectories, providing a robust foundation for the robot's fabrication. Our methodology offers a powerful framework for advancing the design and control of soft robots through integrated simulation and data-driven techniques. This work was done in collaboration with Dezhong Tong, Zhuonan Hao, and Mingchao Liu. Link to the paper: https://lnkd.in/gZfsCYV2 https://lnkd.in/gunfGf36 Link to the open-source code: https://lnkd.in/g_cKU_SH

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Happy to be co-chairing the First international Workshop on Cloud-Network Convergence (CNC), to be held in conjunction with IEEE MASS 2024, in Seoul, South Korea, Sep 23 - 25, 2024. Paper submission deadline - June 21, 2024, 11:59 PM, Anywhere on Earth Time. The proposed workshop will address the challenges of adapting cloud architectures to support the unique requirements of wireless technologies, such as 5G, and the anticipated 6G. It will also explore the potential for wireless network innovations to revolutionize cloud service delivery, emphasizing low latency, high bandwidth, and ubiquitous coverage. Key areas of focus will include network resource optimization, security protocols for wireless cloud access, and scalable solutions to support the growing demand for cloud-connected applications and services. Workshop webpage: https://lnkd.in/dYRd4GY7 Conference webpage: https://lnkd.in/d-R-W9xv

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🏆 Welcome to read in Volume 24 (2023) from Reviews in Cardiovascular Medicine "A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease" by Xiang Ma, Jian Guo Dai et al. #CAD #coronary #machinelearning #XGBoost #SYNTAX #GENSINI 📝 Full Text: https://lnkd.in/gQTAZbD5 Fig. 1. The research process is depicted in the diagram where the raw data is initially subjected to pre-processing and fed into distinct regression and classification algorithms. Following the model training and hyperparameter tuning, the ultimate prediction outcomes are generated, and the SHAP framework is employed for model interpretation.

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ASCE i3ce 2024: Updates 1,2&3/19: Three outstanding presentations from Xiayu Zhao on a range of cutting-edge topics: (1) Adaptive Intelligence for Robot Navigation Efficiency with a Deep-Reinforcement-Learning-Based Cyber-Physical System: This presentation explored innovative advancements in robot navigation by integrating deep reinforcement learning algorithms with cyber-physical systems. This paper aims to improve robots' adaptability and efficiency in complex and dynamic environments, such as construction sites, where traditional navigation systems often fall short. (2) Grounding Large Language Models in Robot Control: Facilitating Human-Robot Collaboration in Construction: This paper addressed the integration of large language models into robot control systems to enhance human-robot interaction. By enabling robots to understand and respond to natural language commands, this research facilitates more intuitive and efficient collaboration between construction workers and robotic systems, aiming to streamline operations and reduce errors. (3) Improving Heat Strain Prediction in Construction Workers: A Transfer Learning Approach to Overcome Variability Challenges: The focus of this paper was on leveraging transfer learning techniques to enhance the accuracy of heat strain prediction models for construction workers. Addressing the challenge of variability among individuals, this approach utilizes existing data to refine models that can more accurately predict when workers are at risk of heat-related illnesses, thereby improving workplace safety and health monitoring. Stay tuned for the conference proceedings publication through ASCE for detailed papers on these topics.

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Our industry collaboration paper is finally published at IEEE Xplore !! Asia Pacific University

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I am excited that the Cardinal Optimizer (COPT) continues to push the boundaries of optimization as a state-of-the-art solver for conic optimization, supporting discrete optimization and a wide range of conic formats. The latest version introduces support for the exponential cone, unlocking incredible potential for modeling dynamic and robust optimization problems. You can download the newest version here: https://lnkd.in/gnD5jU7Y. We have integrated the latest version of COPT with RSOME(https://lnkd.in/gNe2GdJk). This integration significantly extends the modeling capabilities of robust optimization, making it easier than ever to tackle challenging problems. As a strong advocate for exponential cone applications, I am particularly impressed by its ability to tackle complex challenges. For instance, a robust stochastic optimization model for Electric Vehicle Charging using exponential cones significantly outperforms solutions obtained via sample average approximation. This work is detailed in the Operations Research 2023 paper: https://lnkd.in/gFhcdxEN. Additionally, exponential cones have proven highly effective in dynamic optimization problems, particularly when incorporating risk aversion, as demonstrated in a 2024 Operations Research paper: https://lnkd.in/gn_-74zR. More recently, a study accepted in Operations Research uses exponential cones to model dispersion for distributionally robust optimization, showcasing the versatility of this approach. You can find the preprint here: https://lnkd.in/gB57-a4T.

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