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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:20260803T153000
DTEND;TZID=America/New_York:20260803T170000
DTSTAMP:20260912T105849
CREATED:20260727T145526Z
LAST-MODIFIED:20260727T145526Z
UID:152896-1785771000-1785776400@www.ri.cmu.edu
SUMMARY:Simulate to Learn\, Learn to Simulate for Dexterous Robot Control
DESCRIPTION:Abstract:\nSimulation enables robots to learn and evaluate behaviors at scale before real-world deployment. Yet the mismatch between simulation and the physical world remains a fundamental obstacle. This is particularly challenging for dexterous manipulation\, where contact-rich interactions and dynamics variations across objects and robot embodiments are difficult to model. In my thesis research\, I explore how robot learning can scale through simulation and how learned models can make simulation more accurate to the physical world\, through two complementary directions. \nPart I: Differentiable simulation for scalable robot learning. \nFirst\, I present a GPU-parallel differentiable multiphysics simulation and a first-order reinforcement learning algorithm that pairs simulation gradients with entropy regularization\, for smoother policy optimization on locomotion and manipulation tasks. Next\, I introduce hybrid analytic differentiability\, combining implicit differentiation\, auto-differentiation\, and custom analytic Jacobians to compute gradients through contact without modifying forward dynamics. With it\, I develop a production-ready differentiable simulation and show how its gradients support initial value problems\, trajectory optimization\, and system identification. \nPart II: Aligning simulation with the real world across diverse embodiments and tasks. \nFirst\, I introduce an algorithm for iterative real-to-sim alignment. Alongside\, I present a hybrid neural dynamics model that combines learned dynamics correction with analytical inverse dynamics while retaining the simulator’s contact resolution\, to produce physically consistent simulation trajectories. Next\, I build flexible real-time robot I/O infrastructure for synchronized data collection and policy deployment across different robots\, sensors\, and interfaces. \nIn my proposed work\, I will explore how differentiable simulation and differentiable rendering can support real-to-sim reconstruction of simulation environments from multimodal real-world data. In my final project\, I will study how neural dynamics can scale real-to-sim-to-real learning to dexterous hands and humanoid robots requiring high-dimensional continuous control. \n\n\nTogether\, these directions aim to establish a feedback loop in which robot policies and simulators continually improve one another. By turning physical experience into better simulators and using better simulators to train more capable robots\, this loop could scale robot learning across tasks and embodiments in ways that neither simulation nor real-world data can achieve alone.\n\n\nThesis Committee:\nJean Oh (co-chair)\nGuanya Shi (co-chair)\nJeff Ichnowski\nMiles Macklin (NVIDIA)\n\nThesis Link
URL:https://www.ri.cmu.edu/event/simulate-to-learn-learn-to-simulate-for-dexterous-robot-control/
LOCATION:Gates Hillman Center 4405
CATEGORIES:PhD Thesis Proposal,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260804T110000
DTEND;TZID=America/New_York:20260804T120000
DTSTAMP:20260912T105849
CREATED:20260728T154143Z
LAST-MODIFIED:20260728T154143Z
UID:152908-1785841200-1785844800@www.ri.cmu.edu
SUMMARY:Towards Modeling GPS Noise via Raytracing
DESCRIPTION:Abstract:\nAutonomous robot navigation relies heavily on visual-inertial state estimation\, which inherently accumulates drift over extended trajectories. Fusing Global Navigation Satellite System (GNSS) signals mitigates this drift\, but evaluating these systems in hardware is resource-intensive. Therefore\, existing works often rely on simplified\, static noise models to generate GNSS measurements. This thesis presents a raytracing-based GPS simulator integrated with Unity using the Flightmare framework to model realistic signal degradation in complex environments. By computing downward ray intersections from real satellite orbital positions\, the simulator dynamically measures signal attenuation through foliage—using Weissberger’s Modified Exponential Decay model—and building obstructions. Instead of relying on geometric position dilution of precision (DOP)\, the system constructs a measurement noise covariance matrix based on Carrier-to-Noise density ratio loss to yield a Weighted Dilution of Precision (WDOP). Experimental validation in simulated canopy and concrete structure environments\, along with hardware comparisons against a u-blox GNSS receiver on the Carnegie Mellon University campus\, demonstrates that WDOP closely mirrors real-world positioning uncertainty where standard geometric DOP models fail. This work offers a high-fidelity simulation tool for advancing autonomous navigation and sensor fusion in signal-degraded environments. \nThesis Committee:\nWennie Tabib (chair)\nWenshan Wang\nJiaoyang Li\nAlbert Xu
URL:https://www.ri.cmu.edu/event/towards-modeling-gps-noise-via-raytracing/
LOCATION:Newell-Simon Hall 3305
CATEGORIES:MSR Thesis Presentation,Student Talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260810T153000
DTEND;TZID=America/New_York:20260810T163000
DTSTAMP:20260912T105849
CREATED:20260805T190409Z
LAST-MODIFIED:20260805T190409Z
UID:153064-1786375800-1786379400@www.ri.cmu.edu
SUMMARY:Using Sound to Steer Light and Light to Measure Sound
DESCRIPTION:Abstract:   Light and sound are traditionally treated as distinct physical phenomena\, yet their interaction provides a powerful mechanism for manipulating and sensing information across imaging\, communication\, and measurement. This talk explores computational acousto-optic systems that co-design acoustics\, optics\, and signal processing to enable programmable control of light and high-speed optical sensing without mechanical motion. \nFirst\, an acousto-optic structured-light system is presented in which ultrasound generates rapidly varying refractive-index patterns that steer laser beams at megahertz rates. Combined with event-based vision\, this approach projects up to two million structured-light planes per second and enables 3D scanning at up to 1\,000 frames per second. \nNext\, the same acousto-optic principle is applied to underwater optical backscatter communication by dynamically steering retroreflected light toward or away from a receiver using ultrasound. This enables low-power underwater devices to achieve megabit-per-second data transmission without mechanically actuated optics. \nFinally\, the talk investigates the complementary direction of using light to measure sound. By combining a continuous-wave laser\, a high-speed photodetector\, and commodity software-defined radios\, phase-based depth measurements are performed at megahertz rates\, recovering microscopic surface vibrations and acoustic signals from remote objects. \nTogether\, these systems demonstrate that acoustic waves can serve as programmable optical elements while optical measurements provide sensitive probes of acoustic phenomena. By tightly integrating acoustics\, optics\, computational imaging\, and signal processing\, this work establishes a unified framework for building fast\, programmable sensing and communication systems that operate beyond the capabilities of conventional mechanically actuated approaches. \nBio:  Dhawal Sirikonda is a PhD candidate at Dartmouth College working with Prof. Adithya Pediredla in the Rendering and Imaging Science Lab (RISc). He builds next-generation ultra-fast LiDAR sensors and novel imaging systems for high-speed perception and communication applications. His research focuses on designing computational imaging systems by leveraging diverse sensor modalities\, including event cameras\, RGB-D sensors\, and emerging hybrid sensing architectures. His current work explores novel acousto-optic imaging systems for fast scanning and communication applications. He is particularly interested in combining machine learning and physics-based computational methods to develop robust real-world sensing systems. Prior to his PhD\, he completed his Master’s degree working with Prof. P. J. Narayanan at the intersection of 3D Vision and Real-Time Graphics. \nHomepage:   https://dhawal.xyz \nSponsor:\nThe VASC seminar is generously sponsored by HeyGen\, an all-in-one AI-powered video generation platform that leverages advances in computer vision\, generative modeling\, and multimodal learning to make high-quality video creation both scalable and accessible.
URL:https://www.ri.cmu.edu/event/using-sound-to-steer-light-and-light-to-measure-sound/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:Seminar,VASC Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2026/08/8-10-26.jpeg
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260828T120000
DTEND;TZID=America/New_York:20260828T130000
DTSTAMP:20260912T105849
CREATED:20260826T182341Z
LAST-MODIFIED:20260826T182341Z
UID:153300-1787918400-1787922000@www.ri.cmu.edu
SUMMARY:Robot Manipulation Capabilities and Grounded Task Axes
DESCRIPTION:Abstract: \nRobots operating in open-world environments will need to understand the scope of their capabilities\, recognize their limitations\, and determine how those capabilities can be expanded. This talk will review research from the Intelligent Autonomous Manipulation Lab on modeling robot capabilities\, allocating learning resources to expand them efficiently\, and structuring skills for a given scope of tasks. I will also discuss how tactile perception and dexterous hardware can broaden what robots are able to perceive and do. \nThe main focus will be our work on Grounded Task Axes\, a framework for autonomously constructing structured manipulation skills. Rather than representing a skill as a monolithic policy or fixed library element\, Grounded Task Axes compose task-tailored behaviors from controllers defined relative to semantically meaningful object keypoints and axes. Our recent work uses vision and language models to generate these skill structures\, ground them in observed scenes\, and set task-specific parameters. These structured representations support precise controller-level execution\, interpretable human correction\, and generalization across objects and tasks.
URL:https://www.ri.cmu.edu/event/robot-manipulation-capabilities-and-grounded-task-axes/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:Faculty Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260831T153000
DTEND;TZID=America/New_York:20260831T163000
DTSTAMP:20260912T105849
CREATED:20260825T175254Z
LAST-MODIFIED:20260825T175617Z
UID:153282-1788190200-1788193800@www.ri.cmu.edu
SUMMARY:My Bitter Lesson with Computer Graphics
DESCRIPTION:Abstract:  In his essay “The Bitter Lesson\,” Richard Sutton argued that general methods leveraging computation ultimately outperform hand-crafted ones. In this talk\, I share my own version of this lesson\, learned the hard way over a decade in computer graphics. Where does the bitter lesson apply to graphics\, and where does it not? I argue that the answer depends on whether a problem genuinely requires 3D\, physics\, and control\, or if it simply produces 2D pixels. I will also share a few advices for graduate students starting their research today\, showing which directions will compound in value over a decade\, and which will be quietly subsumed by the next scale-up. \nBio:  Giljoo Nam is a research scientist at Meta focused on Physical AI\, building systems that understand the structure and behavior of the 3D physical world. His research bridges computer vision and graphics\, with focus areas including generative AI\, 3D reconstruction\, motion tracking\, inverse rendering\, computational imaging\, and human modeling. He earned his Ph.D. in Computer Science from KAIST in 2019.. \nHomepage:   https://giljoonam.github.io/ \n  \nSponsor: \nThe VASC seminar is generously sponsored by HeyGen\, an all-in-one AI-powered video generation platform that leverages advances in computer vision\, generative modeling\, and multimodal learning to make high-quality video creation both scalable and accessible.
URL:https://www.ri.cmu.edu/event/my-bitter-lesson-with-computer-graphics/
LOCATION:3305 Newell-Simon Hall
CATEGORIES:Seminar,VASC Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2026/08/8-31-26.jpg
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