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X-WR-CALNAME:Robotics Institute Carnegie Mellon University
X-ORIGINAL-URL:https://www.ri.cmu.edu
X-WR-CALDESC:Events for Robotics Institute Carnegie Mellon University
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DTSTART;TZID=America/New_York:20251003T143000
DTEND;TZID=America/New_York:20251003T153000
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SUMMARY:Neural Certificates for Safe Robotic System Planning and Control
DESCRIPTION:Abstract:\nAchieving safety\, scalability\, and high performance in complex systems\, such as multi-agent systems (MAS) control\, is a central challenge in many real-world robotic deployments due to its computational complexity as a large-scale constrained optimal control problem. To address this\, we introduce a novel graph control barrier function (GCBF) as a core tool for large-scale distributed safe control\, which guarantees safety for arbitrarily large MAS with only local observations. For MAS with known dynamic models\, we present a self-supervised learning framework that can jointly learn GCBF and distributed control policies that consider actuation limits. For MAS with unknown dynamics\, we discuss how to blend GCBF in multi-agent reinforcement learning (MARL) to achieve high-performance and safe distributed policies.\n\nBio:\nChuchu Fan is an Associate Professor (pre-tenure) in the Department of Aeronautics and Astronautics (AeroAstro) and Laboratory for Information and Decision Systems (LIDS) at MIT. Before that\, she was a postdoc researcher at Caltech and got her Ph.D. at the University of Illinois at Urbana-Champaign. She earned her bachelor’s degree from Tsinghua University. Her research group\, the Realm at MIT\, works on developing computational tools that integrate rigorous mathematics into machine learning and AI for the design\, analysis\, and verification of safe\, large-scale\, and complex systems. Chuchu is the recipient of an NSF CAREER Award\, an AFOSR Young Investigator Program (YIP) Award\, an ONR YIP Award\, and the 2020 ACM Doctoral Dissertation Award.
URL:https://www.ri.cmu.edu/event/neural-certificates-for-safe-robotic-system-planning-and-controlri-seminar-w-chuchu-fan/
LOCATION:1403 Tepper School Building
CATEGORIES:RI Seminar,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2025/09/ChuchuFan-042021.jpg
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DTSTART;TZID=America/New_York:20251010T143000
DTEND;TZID=America/New_York:20251010T153000
DTSTAMP:20260922T083934
CREATED:20250902T163332Z
LAST-MODIFIED:20251010T231714Z
UID:148665-1760106600-1760110200@www.ri.cmu.edu
SUMMARY:A Manipulation Journey
DESCRIPTION:Abstract:\nThe talk will revisit my career in manipulation research\, focusing on projects that might offer some useful lessons for others. We will start with my beginnings at the MIT AI Lab and my MS thesis\, which is still my most cited work\, then continue with my arrival at CMU\, a discussion with Allen Newell\, an exercise to envision a coherent research program\, and how that led to a second and third childhood. The talk will conclude with some discussion of lessons learned.\n\nBio:\nMatt has spent 50 years conducting research in Artificial Intelligence and Robotics\, starting as a student in the MIT AI Lab where he earned the BS\, MS\, and PhD degrees. He spent much of his career at CMU’s Robotics Institute\, where he was the founder and co-director of the Manipulation Laboratory\, and for ten years served as the Director of the Robotics Institute. Matt’s group studied the basic physics governing grasping and manipulation\, and demonstrated that sophisticated grasping and manipulation can be produced by the simple and robust grippers used in industrial automation.\n\nMason is a Fellow of the AAAI\, AAAS\, ACM\, and IEEE\, and a winner of the IEEE R&A Society’s Pioneer Award\, and the IEEE Technical Field Award in Robotics and Automation (the R&A Prize).\n\nMatt now focuses his attention on logistics and warehouse robotics\, helping to produce industry-leading solutions at Berkshire Grey.
URL:https://www.ri.cmu.edu/event/a-manipulation-journey/
LOCATION:1403 Tepper School Building
CATEGORIES:RI Seminar,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2025/09/unnamed.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251024T143000
DTEND;TZID=America/New_York:20251024T153000
DTSTAMP:20260922T083934
CREATED:20250902T163833Z
LAST-MODIFIED:20251024T220347Z
UID:148668-1761316200-1761319800@www.ri.cmu.edu
SUMMARY:Bringing Dexterity to Robot Hands in the Real World
DESCRIPTION:Abstract:  Dexterous manipulation is a grand challenge of robotics\, and fine manipulation skills are required for many robotics applications that we envision.   In this overview talk\, I will discuss my view of some major factors that contribute to dexterity and discuss how we can incorporate them into our robots and systems.\n\nBio:  Nancy Pollard is a Professor in the Robotics Institute and the Computer Science Department at Carnegie Mellon University. She received her PhD in Electrical Engineering and Computer Science from the MIT Artificial Intelligence Laboratory\, where she developed grasp and manipulation planning algorithms for the Stanford/JPL and Utah/MIT dexterous hands. Prof. Pollard spent the next few decades studying human and robot dexterity\, with emphasis on bringing human manipulation strategies with performance guarantees to humanoid robots with dexterous hands.  She received the NSF CAREER award for research on “Quantifying Humanlike Enveloping Grasps”\,  the Okawa Research Grant for “Studies of Dexterity for Computer Graphics and Robotics\,” and was a recent recipient of an NSF Convergence Accelerator award for “Bio-Inspired Design of Robot Hands for Use-Driven Dexterity.”   She has led the development of several generations of dexterous soft robotic hands\, is a founder of FuturHand Robotics and leads the CMU Foam Hands Laboratory.
URL:https://www.ri.cmu.edu/event/bringing-dexterity-to-robot-hands-in-the-real-world/
LOCATION:1403 Tepper School Building
CATEGORIES:RI Seminar,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2025/09/nsp-crop.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251031T143000
DTEND;TZID=America/New_York:20251031T153000
DTSTAMP:20260922T083934
CREATED:20250902T164414Z
LAST-MODIFIED:20251030T145800Z
UID:148671-1761921000-1761924600@www.ri.cmu.edu
SUMMARY:Toward Generalist Humanoid Robots: Recent Advances\, Opportunities\, and Challenges
DESCRIPTION:Abstract: In an era of rapid AI progress\, leveraging accelerated computing and big data has unlocked new possibilities to develop generalist AI models. As AI systems like ChatGPT showcase remarkable performance in the digital realm\, we are compelled to ask: Can we achieve similar breakthroughs in the physical world — to create generalist humanoid robots capable of performing everyday tasks? In this talk\, I will outline our data-centric research principles and approaches for building general-purpose robot autonomy in the open world. I will present our recent work leveraging real-world\, synthetic\, and web data to train foundation models for humanoid robots. Furthermore\, I will discuss the opportunities and challenges of building the next generation of intelligent robots.\n\nBio: Yuke Zhu is an Associate Professor in the Computer Science Department of UT-Austin\, where he directs the Robot Perception and Learning (RPL) Lab. He is also a Director and Distinguished Research Scientist at NVIDIA Research\, where he co-leads the Generalist Embodied Agent Research (GEAR) lab. He focuses on developing intelligent algorithms for generalist robots and embodied agents to reason about and interact with the real world. He obtained his Ph.D. degree from Stanford University. He received the NSF CAREER Award\, the IEEE RAS Early Academic Career Award\, and various faculty fellowships and research awards from Amazon\, JP Morgan\, and Sony Research.
URL:https://www.ri.cmu.edu/event/toward-generalist-humanoid-robots-recent-advances-opportunities-and-challenges/
LOCATION:1403 Tepper School Building
CATEGORIES:RI Seminar,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2025/09/yukezhu.jpg
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