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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
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DTSTAMP:20260911T184854
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
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DTSTART;TZID=America/New_York:20260804T110000
DTEND;TZID=America/New_York:20260804T120000
DTSTAMP:20260911T184854
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
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