جستجوی استاد راهنمای دانشگاهی

Roberto Martin-Martin is an Assistant Professor of Computer Science at the University of Texas at Austin, where he leads the Robot Interactive Intelligence (RobIN) Lab. His research bridges robotics, computer vision, and machine learning to enable robots to operate autonomously in human-centric environments like homes and offices. Previously, he was a Postdoctoral Scholar at the Stanford Vision and Learning Lab working with Fei-Fei Li and Silvio Savarese, and an AI Researcher at Salesforce AI. Education: Ph.D. and M.Sc. in Robotics from Technische Universität Berlin (TUB), advised by Professor Oliver Brock B.Sc. from Universidad Politécnica de Madrid Dr. Martin-Martin's research focuses on developing AI algorithms that combine reinforcement learning and imitation learning with advanced planning and control to address core challenges in robot perception. His work spans mobile and whole-body manipulation, dexterous and contact-rich interactions, and long-horizon tasks in unstructured environments. He takes inspiration from human cognition through psychology and cognitive science to develop solutions for skills ranging from simple pick-and-place operations to complex tasks like cooking and furniture assembly. His recent publications demonstrate a strong trend toward enabling robots to learn from human demonstrations, particularly through video, and to safely adapt these demonstrations to their own morphology. There's significant emphasis on mobile manipulation, bimanual tasks, and developing hardware that supports robust robot learning through trial and error. His work shows increasing integration of large language models and vision-language models to enhance robot understanding and task execution. Scientific Awards: RSS Pioneer (2020) Winner of Amazon Picking Challenge (2015) RSS Best Systems Paper Award (2016) ICRA Best Paper Award IROS Best Mechanism Award Amazon Faculty Award AAAI Young Faculty IJCAI Early Faculty Nominated for Best Paper at IROS (2014, 2017) Dr. Martin-Martin advises PhD students including Arpit Bahety, who is working on mobile manipulation and learning. He serves as Chair of the IEEE Technical Committee on Mobile Manipulation and is a co-founder of QueerInRobotics. His research is supported by industry partnerships and academic funding sources that enable his lab to develop both hardware and software innovations in robotics. He directs the Robot Interactive Intelligence (RobIN) Lab at UT Austin, which takes a holistic approach to robot intelligence, developing both the hardware (like the BaRiFlex gripper) and software frameworks necessary for robots to learn from interaction. The lab's research addresses the full pipeline from perception to action, with particular emphasis on learning from human demonstrations, safe exploration, and adapting to novel objects and environments.

• Robotics
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Kaiyu Hang is an Assistant Professor in the Department of Computer Science at Rice University, where he directs the Robotics and Physical Interactions Lab (RobotΠ Lab). His research spans multiple domains of robotics with a focus on physical interaction systems. Before joining Rice, he completed his postdoc at Yale University, earned his Ph.D./M.Sc. at KTH Royal Institute of Technology, and received his B.Eng. from Xi'an Jiaotong University. His research interests include robotic manipulation, grasping, in-hand manipulation, optimization, planning, learning, estimation, and control systems. He develops algorithms that enable robots to physically interact with other robots, people, and the world across scales from small grasping tasks to large-scale dual-arm and multi-robot manipulation systems. His work has practical applications in factories, kitchens, hospitals, warehouses, and construction sites. His recent publications demonstrate strong trends in in-hand manipulation techniques, energy-efficient drone operations, and benchmarking frameworks for robotic grasping. The 2025 IROS papers accepted highlight his leadership in developing standardized competition frameworks for evaluating robotic manipulation capabilities across diverse hardware platforms. ASME Rising Star of Mechanical Engineering (2024) NSF CAREER Award (2023) Multiple finalist awards at IEEE-RAS Humanoids and ICRA conferences Junior Fellowship Award from Institute for Advanced Study, HKUST (2017-2018) As an educator, he has taught multiple robotics courses including COMP 462/562: Introduction to Modern Robotics and COMP 461: Senior Design in A Robotized World. He serves as Faculty Advisor for the Rice Robotics Club and participates in graduate admissions. His lab actively recruits Ph.D. students and offers research opportunities for undergraduate and master's students who have completed core robotics courses.

• Robotics
• Robotic Manipulation
• Grasping
• +۸ مورد دیگر

Tetsuya Ogata is a Professor at the Faculty of Science and Engineering, School of Fundamental Science and Engineering at Waseda University. He also holds joint appointments as a Fellow at the Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), and as a visiting professor at the National Institute of Informatics (NII). Professor Ogata serves as director of the Institute of AI and Robotics at Waseda University, chairperson of the AI Robot Association (AIRoA), and research supervisor of JST CREST "Fundamentals and Core Technologies for Embodied AI" since 2025. Professor Ogata received his B.S., M.S., and D.E. degrees in mechanical engineering from Waseda University in 1993, 1995, and 2000, respectively. He began his academic career as a Research Associate at Waseda University (1999-2001), followed by positions as a Research Scientist at RIKEN Brain Science Institute (2001-2003), Lecturer (2003-2005) and Associate Professor (2005-2012) at Kyoto University's Graduate School of Informatics, before becoming a Professor at Waseda University in 2012. He was also a JST PRESTO Researcher from 2009 to 2015. Professor Ogata's research focuses on cognitive developmental robotics, deep predictive learning, and the integration of vision, language, and action (VLA) for human-robot interaction. His work explores how robots can learn through experience and interaction with humans and environments, developing models that allow for flexible behavior generation and adaptation. His research has significant implications for creating robots that can understand human intentions, collaborate effectively in real-world settings, and continuously learn from their experiences. The integration of multimodal information processing with predictive models forms the core of his approach to embodied artificial intelligence. Analysis of Professor Ogata's recent publications reveals a strong focus on deep predictive learning frameworks that enable robots to anticipate future states and generate appropriate actions. His research spans multiple domains including soft object manipulation, bimanual coordination, transparent object grasping, and integration of large language models with robotic systems. A notable trend is the increasing emphasis on multimodal integration, combining visual, tactile, and linguistic information to create more robust and adaptable robotic systems. His recent work also shows growing interest in the application of foundation models to robotics, aiming to create more general-purpose robotic capabilities. Best Paper Award, Advanced Robotics, The Robotics Society of Japan (2024) Best paper award Nomination Finalist, IEEE/SICE International Symposium on System Integration (2024) Frontiers of Science Awards, The International Congress for Basic Science (2023) Minister of Education, Culture, Sports, Science and Technology Award (2023) Fellow, Japan Society of Mechanical Engineering (2023) Fellow, The Society of Instrument and Control Engineers (2022) Fellow, The Robotics Society of Japan (2022) IBM Academic Awards (2017) Professor Ogata has been instrumental in establishing collaborative research frameworks between academia and industry, serving in leadership roles for multiple professional organizations including the Robotics Society of Japan, Japanese Society for Artificial Intelligence, and Japan Deep Learning Association. His research has been supported by significant grants including JST CREST funding for "Fundamentals and Core Technologies for Embodied AI." He has mentored numerous researchers who have gone on to contribute to the field of robotics and AI, with many of his former students now holding positions in leading academic and industrial research institutions. Professor Ogata directs the Ogata Laboratory at Waseda University, which focuses on cognitive developmental robotics and deep predictive learning. The lab maintains strong collaborations with industry partners including major robotics companies and technology firms. In 2025, he played a key role in establishing the AI Robot Association (AIRoA), which brings together companies like Toyota, Nissan, and KDDI to advance the development of practical AI robotics technologies. His research group is known for its interdisciplinary approach, combining insights from neuroscience, cognitive science, and engineering to create more human-like robotic capabilities.

• Cognitive Robotics
• Developmental Robotics
• Human-Robot Interaction
• +۵ مورد دیگر

Kyle B. Reed serves as an Associate Professor in the Department of Mechanical Engineering at the University of South Florida's College of Engineering. His academic career at USF has progressed from Assistant Professor (2009-2016) to his current position as Associate Professor (2016-present), following postdoctoral research at Johns Hopkins University. He teaches specialized courses including Haptics (EML 4593/6594), Mechanical Controls, and Advanced Engineering Mathematics. Dr. Reed earned his Ph.D. (2007) and M.S. (2004) in Mechanical Engineering from Northwestern University, and his B.S. in Mechanical Engineering from the University of Tennessee-Knoxville (2001). Prior to his faculty position, he completed postdoctoral research at Johns Hopkins University's Laboratory for Computational Sensing and Robotics (2007-2009) and worked as a researcher at Los Alamos National Laboratory (1998-2001). His research focuses on rehabilitation engineering, haptics, human-robot interaction, and medical robotics, with applications in medical devices and rehabilitation technologies. The REED Lab (Rehabilitation Engineering and Electromechanical Design Lab), which he directs, develops innovative solutions for human motion analysis and assistive technologies. His work bridges fundamental engineering principles with clinical applications, particularly in stroke rehabilitation and assistive device development. Analysis of his publication record shows consistent contributions to haptics research, human-robot interaction, and rehabilitation engineering. His work spans theoretical investigations of human motor control to practical applications in medical devices, with publications appearing in venues like IEEE Transactions on Haptics, EMBC, and Haptics Symposium. Recent work emphasizes wearable haptic devices, rehabilitation robotics, and human factors in medical technology. Developed the Gait Enhancing Mobile Shoe (GEMS) prototype during postdoctoral work Created minimally invasive steerable needle system for biopsies at Johns Hopkins Established REED Lab focusing on rehabilitation engineering and haptic technologies Developed innovative haptic devices for communication, rehabilitation, and education Dr. Reed actively mentors students through the REED Lab, supervising both graduate and undergraduate researchers. His teaching philosophy emphasizes building intuition while providing frameworks for logical problem-solving. He incorporates substantial project components into his courses, with students regularly developing haptic devices and robotics applications that sometimes lead to publications. The lab maintains strong outreach connections, working with K-12 students to promote engineering education. The REED Lab, located in the Interdisciplinary Research Building Room 114 on USF's Tampa campus, serves as the hub for his research activities. Current projects include wearable haptic devices for communication, diagnostic tools for Parkinson's disease, and rehabilitation technologies for gait analysis. The lab maintains strong connections with both clinical partners and industry, facilitating translational research from concept to application.

• Rehabilitation engineering
• Haptics
• Human-robot interaction
• +۴ مورد دیگر

Mark A. Minor is an Associate Professor in the Department of Mechanical Engineering at the University of Utah's College of Engineering, where he serves as Director of the Robotics Systems Lab and Coordinator of the Robotics Track within Mechanical Engineering. His research spans multiple domains of robotics including climbing robots, terrain adaptable mobile robots, virtual interfaces, autonomous vehicles, and flying robots. The Robotics Systems Lab under his direction synergizes design, modeling, and control to create novel robotic embodiments with enhanced adaptability, mobility, and immersion. Dr. Minor's research focuses on the synergistic integration of design, modeling, and control of robotic systems. His lab specializes in several types of robotic systems including climbing robots, terrain adaptable mobile robots, virtual interfaces, autonomous vehicles, and flying robots. With extensive expertise in the design and control of under-actuated nonholonomic systems, kinematic motion control, dynamic motion control, state estimation, sensor development, and data fusion, his work pushes the boundaries of what robotic systems can achieve in challenging environments. His research portfolio demonstrates a consistent focus on practical robotic applications across multiple domains. Recent work shows increasing emphasis on immersive virtual environments with wind, olfactory, and thermal displays, as well as sophisticated control systems for autonomous vehicles and terrain-adaptive locomotion. The integration of haptic feedback, multi-sensory stimulation, and soft robotics components represents an emerging trend in his research trajectory, bridging the gap between physical robotics and human perception. Dr. Minor has successfully advised numerous graduate students through completion of their degrees, with alumni now working at institutions including Harbin Institute of Technology, University of Utah, Orbital Sciences Corporation, and Western Digital Corporation. His research has been supported by prestigious sponsors including the National Science Foundation, NASA, Army Night Vision Lab, and industry partners such as ATK Launch System and Kairos Autonomi. The Robotics Systems Lab, directed by Dr. Minor, maintains several active research projects including Hybrid Mobility Research (investigating robots capable of rolling, walking, and climbing), Autonomous Vehicle Research (particularly related to the DARPA Urban Challenge), Traction Sensing and Control in Wheeled Mobile Robots, Immersive Virtual Environments (including the TreadPort Active Wind Tunnel), and Compliant Framed Modular Mobile Robots. The lab continues to push the boundaries of robotic mobility, control, and human-robot interaction.

• Robotics
• Mobile Robotics
• Autonomous Vehicles
• +۵ مورد دیگر

Hiroyuki Ishii is a Professor at Waseda University's School of Creative Science and Engineering, Faculty of Science and Engineering. With over 15 years of academic experience at Waseda University, he was promoted to full Professor in 2022 after serving as Associate Professor from 2016-2022. His research spans robotics, control systems, and mechanical engineering, with particular expertise in soft robotics and continuum manipulators. His educational background includes: Ph.D. from Waseda University (2004-2007) Master's degree from Waseda University Graduate School of Science and Engineering (2002-2004) Bachelor's degree from Waseda University School of Science and Engineering (1998-2002) Professor Ishii's research focuses on Robotics and Intelligent Mechanics and Mechanical Systems , with particular emphasis on soft robotics, continuum manipulators, and human-robot interaction. His work bridges theoretical control systems with practical applications in medical robotics, rehabilitation devices, and inspection systems. He has pioneered novel approaches in pneumatic actuation, growing robots, and bimanual continuum robot control, demonstrating exceptional creativity in solving complex mechanical challenges. His recent publications reveal a strong trend toward medical applications of robotics, particularly in surgical assistance and rehabilitation. There's also significant work on soft growing robots and continuum manipulators with innovative actuation mechanisms. His research shows increasing interdisciplinary collaboration, spanning mechanical engineering, computer vision, and biomedical applications. His notable awards include: Best Paper Award Finalist at IEEE ICMA2024 The Robotics Society of Japan 3rd Outstanding Research and Technology Award (2022) Minister of Education, Culture, Sports, Science and Technology Young Scientists Award (2018) Advanced Robotics Best Paper Award (2015) ICRA2014 Best Cognitive Robotics Paper Award Finalist Professor Ishii actively participates in research committees and professional societies, serving as Director of the Robotics Society of Japan since 2024. His research has received substantial funding through competitive grants, supporting his work on innovative robotic systems with practical applications in healthcare, infrastructure inspection, and human-robot collaboration. His laboratory develops specialized robotic systems including small mobile robots for animal interaction, soft growing robots, and continuum manipulators for surgical applications. The team combines mechanical design expertise with advanced control algorithms to create robots capable of operating in complex environments.

• Robotics
• Control Systems
• Mechanical Engineering
• +۴ مورد دیگر

Olivier White is a Professor at the University of Burgundy's Faculty of Sports Science (UFR STAPS), where he maintains his laboratory at Espace Marey. With extensive teaching experience across engineering and neuroscience disciplines, he supervises MSc and PhD students while leading a multidisciplinary research program focused on motor control mechanisms under normal and altered gravity conditions. His educational background spans civil engineering and neuroscience, with a BSc (1997), MSc in Computer Science (2000, summa cum laude), DEA in Applied Mathematics (2002), and PhD in Neuroscience and Applied Mathematics (2007), all from Louvain School of Engineering, Belgium. He completed his Habilitation in 2016 with research titled 'From basic motor control to applications' and earned French Qualification for the Neuroscience Section in 2020. White's research investigates how the brain controls diverse actions through innovative approaches including 3D virtual reality, haptic devices, and gravitational manipulation via parabolic flights and centrifuges. His work addresses fundamental questions about sensory information in learning, motor skill generalization, and strategy selection. He employs motion capture, force measurements, electromyography, fMRI, and mathematical modeling approaches including optimal control and fractal analyses. His publication record reveals a consistent focus on gravity-related motor adaptation, with recent work exploring sensorimotor control in inverted postures, resonance phenomena in rhythmic movements, and spaceflight effects on operational performance. His research bridges theoretical physics with practical neuroscience applications, particularly in space physiology and rehabilitation contexts. Dr. White maintains an active teaching portfolio across multiple institutions including University of Burgundy, King's College London, and ESTP Engineering School. His courses range from basic computer science to advanced topics in computational motor control, space physiology, and deep learning applications. Motor control and learning mechanisms Altered gravity effects on human movement Virtual reality and haptic applications Fractal analysis of movement patterns Space physiology and neurorehabilitation Mathematical modeling of motor control He actively supervises research projects at undergraduate and graduate levels, building a laboratory that combines behavioral, neuroimaging, and mathematical methods to understand movement planning and execution. Through international collaborations, particularly in space-related research, he develops applications with societal benefits in rehabilitation and human performance enhancement.

• Motor control and learning
• object manipulation and dexterity
• upper limb movements
• +۱۲ مورد دیگر

Li Yi is a Professor in the Department of Computer Science at Tsinghua University's School of Information Science and Technology, where they lead cutting-edge research at the intersection of computer vision, 3D graphics, and robotics. Their work focuses on advancing neural rendering, point cloud processing, and embodied AI with applications in human-object interaction and robotic manipulation. Research interests span Computer Vision , 3D Graphics , Robotics , Point Cloud Processing , Neural Rendering , and Human-Object Interaction . Recent work explores language-grounded spatial reasoning, dexterous manipulation, and 4D dynamic content generation, with publications appearing in top venues like CVPR, ICCV, and NeurIPS. Their research bridges theoretical advances with practical applications in embodied AI systems. The publication trends reveal a strong focus on neural rendering techniques (particularly NeRF variants), embodied AI for robotic manipulation , and multimodal understanding integrating vision, language, and action. Recent work increasingly incorporates large language models and focuses on generalizable approaches that transfer from simulation to real-world settings. As an advisor, Professor Li has mentored numerous students including Yunze Liu, Xueyi Liu, Zekun Qi, and Runpei Dong, who frequently appear as first authors on collaborative publications. Their research has been supported by significant grants enabling work on human-robot interaction, 3D scene understanding, and embodied AI systems. Professor Li leads a research group focused on developing comprehensive frameworks for spatial reasoning, object manipulation, and dynamic scene understanding. The team works on creating benchmarks like TACO for tool-action-object understanding and developing systems like MobileH2R for human-robot handover tasks. Current work emphasizes real-world applicability with a focus on generalizable solutions that work across diverse settings.

• Computer Vision
• 3D Graphics
• Robotics
• +۴ مورد دیگر

Joni Pajarinen is an Associate Professor in the Department of Electrical Engineering and Automation at Aalto University. His research focuses on reinforcement learning, robotics, and autonomous systems with applications in multi-agent coordination, control systems, and object-centric learning. He has contributed to advancements in policy optimization, imitation learning, and underwater vehicle control. Key research areas include: Reinforcement learning algorithms for robotics Multi-agent systems and cooperative control Autonomous underwater vehicle navigation Machine learning for industrial applications His recent work emphasizes scalable reinforcement learning frameworks, such as AgentMixer for correlated policy factorization and entropy-regularized task representations. He explores vision-language models (e.g., RGB-Th-Bench) and large-scale motion datasets (RP1M) for humanoid robotics. Publications span top venues like AAAI, ICLR, and IEEE Robotics and Automation Letters. His research bridges theory and real-world applications in robotics, marine engineering, and manufacturing automation.

• Robotics
• Reinforcement Learning
• Multi-Agent Systems
• +۴ مورد دیگر

Sehoon Ha is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology's College of Computing, with affiliations to the Robotics and Matter & Systems research initiatives. He holds a Ph.D. in Computer Science from Georgia Tech (2015) and a B.S. from KAIST (2009). His research spans robotics, AI, and computer graphics, with core focuses on: Legged locomotion and humanoid control Reinforcement learning for robotic systems Human-robot interaction and assistive technologies Motion synthesis and character animation Recent publications (2024-2025) demonstrate strong emphasis on safe reinforcement learning, vision-language navigation, and human-robot collaboration, with frequent appearances at ICRA, IROS, and RSS conferences. Awards & Honors: NSF CAREER Award (2024) Best Cognitive Paper Award, ICRA 2024 iGibson Challenge winner, CVPR 2021 Research Leadership: Secured $7M collaborative grant for mobile humanoid development (with Samsung/ETRI/KAIST), with additional funding from Google, Meta, Amazon, and ETRI. Leads a 17-member research group including PhD and MS students across robotics and ML domains.

• robotics
• artificial intelligence
• computer graphics
• +۴ مورد دیگر

Yuke Zhu is an Assistant Professor in the Department of Computer Science at The University of Texas at Austin, directing the Robot Perception and Learning Lab . His research spans robotics, computer vision, and machine learning, focusing on general-purpose robot autonomy through perception-action integration. Key Affiliations: University of Texas at Austin (Department of Computer Science), Stanford University (Ph.D. alumnus) Research Focus His work explores: Robotic perception and decision-making in unstructured environments Embodied AI frameworks for active, situated agents Sim-to-real transfer and humanoid whole-body control Collaborative methods drawing from neuroscience and philosophy Scientific Contributions Google Scholar profile with 15+ recent publications on visuomotor policies, deformable object manipulation, and robotic foundation models Recipient of the IEEE RAS Early Career Award Developer of open-source frameworks like robosuite and RoboCasa for robotic simulation Lab Leadership The RPL Lab actively investigates robotics and embodied AI, emphasizing: Closing perception-action loops for autonomous systems Computational frameworks for open-world interactions Collaborative research with six ICRA 2025 papers

• Machine Learning
• Embodied AI
• Robot Perception
• +۵ مورد دیگر

Dr. Emily Collins is a Dame Kathleen Ollerenshaw Fellow in the Department of Computer Science at the University of Manchester. Her work focuses on Human-Robot Interaction (HRI), combining experimental rigor with interdisciplinary approaches. She holds a Philosophy BA (Cardiff University), MSc in Experimental Psychology (Sussex University), and a PhD in Psychology and Social Robotics (University of Sheffield). Her research explores trustworthiness, verification, and socio-political factors in Robotics and AI deployment. Key career milestones include EPSRC Doctoral Prize Fellowships, roles in the RAIN and ORCA projects addressing hazardous environment robotics, and leadership at Northeastern University's Institute for Experiential Robotics. She has contributed to neurorehabilitation robotics, verification frameworks for industrial robots, and cross-cultural HRI studies in Japan. Her awards include the Dame Kathleen Ollerenshaw Fellowship and Chartered Psychologist status from the British Psychological Society. Current projects emphasize ethical HRI design and the psychological underpinnings of human-robot relationships, with a focus on mitigating deployment barriers through interdisciplinary collaboration.

• Human-Robot Interaction
• Biomimetic Robotics
• Trustworthiness in AI
• +۳ مورد دیگر

Camilo Perez Quintero is a Research Fellow at the University of British Columbia's CARIS laboratory, specializing in Human-Robot Interaction systems. His current work includes developing augmented reality multimodal interfaces for aerospace manufacturing in collaboration with the German DLR and creating sidewalk navigation solutions for delivery robots through a Mitacs industry partnership. Education: Ph.D. in Computing Science, University of Alberta, Canada (2017) His research centers on communication mechanisms for human-robot collaboration, with expertise in augmented reality interfaces, haptic feedback systems, and gesture-based control. He excels at translating theoretical concepts into functional prototypes through multidisciplinary teamwork, focusing on mission-critical applications in manufacturing and urban environments where safety and intuitive interaction are paramount. Analysis of his 15 most recent publications (2017-2022) reveals consistent innovation in robot trajectory programming through multimodal interfaces, with significant contributions to safe human-robot collaboration in manufacturing via virtual barriers and pedestrian-aware navigation for sidewalk robots. His work bridges computer vision, robotics, and human-centered design, demonstrating particular strength in integrating tactile feedback with AR/VR systems for industrial applications. Grants and Programs: Mitacs Accelerate program developing HRI systems for sidewalk mobile robots with industrial partner German DLR collaboration on aerospace manufacturing interfaces He operates within UBC's CARIS laboratory, collaborating with mechanical engineering researchers and international partners to advance practical robotics solutions for real-world deployment in constrained environments.

• Human-Robot Interaction
• Augmented Reality
• Virtual Reality
• +۳ مورد دیگر

Jack Collins is a Researcher and AI Research Scientist at Collaborative Robotics, focusing on robotics foundation models for collaborative robots. Previously, he served as a Postdoctoral Researcher at the University of Oxford's Applied Artificial Intelligence Lab and completed his PhD at Queensland University of Technology (QUT) in collaboration with CSIRO. His research emphasizes sim-to-real methods, robot simulation, and bridging the 'Reality Gap.' He holds a Bachelor of Engineering (Mechatronics) with First Class Honours from QUT. Education: PhD in Robotics (2018-2022), QUT/CSIRO BEng (Mechatronics) (Honours) (2014-2017), QUT Research Interests: Robot simulation and sim-to-real transfer Bimanual/mobile robotic manipulation Generative models and world modeling Task planning for long-horizon tasks Evolutionary robotics and component design Publications: Over 15 peer-reviewed articles focusing on simulation realism, robotic manipulation benchmarks, and generative modeling techniques. Key work includes the COMBO-Grasp framework and TWIST distillation method for sim-to-real transfer. Lab Affiliations: QUT Centre for Robotics (QCR), Oxford Robotics Institute's Applied AI Lab.

• Robot Simulation
• Sim-to-Real Methods
• Robotics Foundation Models
• +۷ مورد دیگر

Mohamed Kari is a Presidential Postdoctoral Fellow at the Department of Computer Science, Princeton University , and an Associate Member at the University of Duisburg-Essen 's Integrated Information Systems research group. His work bridges Mixed Reality , Human-Computer Interaction , and Machine Learning , with a focus on semantically coherent, sensor-rich systems. PhD in Computer Science from University of Duisburg-Essen (with highest distinction) Fulbright Full Grant recipient for studies in the U.S. Research Interests span immersive technologies, sensor integration, and context-aware systems. He investigates how to seamlessly blend virtual actions with physical environments through Scene Responsiveness and develops audio-augmented reality applications like SoundsRide for automotive contexts. Article Trends reveal his focus on environmental manipulation in mixed reality affordance-synchronized user experiences pose-aware object substitution mobile and constrained VR input methods automotive context modeling Scientific Awards include Best Paper Awards at ACM UIST 2021 and 2023 Honorable Mention , as well as recognition for his dissertation with the highest distinction. Thesis Supervision at UDE involves rigorous requirements: English-language theses using LaTeX , original research with structured problem statements, and mandatory video presentations of contributions. He has guided students in topics like Intra-Company Networking Platforms and Automated UI Testing . Laboratory Affiliations include Princeton's Situated Interactions Lab , ETH Zurich's Sensing, Interaction & Perception Lab , and industry labs at Apple , Meta , and Porsche Emerging Tech Research .

• Human-Computer Interaction
• Mixed Reality
• Computer Graphics
• +۵ مورد دیگر