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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:20260911T100000
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DTSTAMP:20260911T184852
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UID:153484-1789120800-1789126200@www.ri.cmu.edu
SUMMARY:From Simulation to the Real World: A Multi-Level System for Object Navigation with Vision-Language Models
DESCRIPTION:Abstract: \nObject navigation (ObjectNav) asks a robot to find an instance of a target object category in an unknown environment\, which demands perception\, spatial reasoning\, and long-horizon decision making at once. Today’s autonomous robots excel at mapping and moving through space yet lack high-level semantic intelligence\, while vision-language models (VLMs) offer rich commonsense reasoning but limited 3D spatial grounding and long-term spatial consistency. Most existing VLM-based navigation methods treat the model as a black-box oracle\, querying it at every step on unstructured local observations\, which leads to redundant backtracking\, inefficient exploration\, and brittle behavior outside clean simulation. This thesis argues that ObjectNav is a system-level problem rather than a single-policy learning task: its sub-challenges of semantic understanding\, complex spatial structure\, and long-horizon planning should be explicitly decoupled and handled by cooperating modules\, with the VLM asked to reason only at the level where it is reliable. We build such a system and carry it step by step from simulation to floor-scale\, cross-embodiment deployment in the real world. \nWe first develop the core of this system in simulation\, where the robot incrementally organizes what it has seen into a structured scene representation and the VLM reasons only at a high level over it\, while efficient geometry-based exploration handles fine-grained navigation. This design achieves state-of-the-art success rate and navigation efficiency across four widely used benchmarks. We then bring the system into the real world and extend it into three cooperating levels that decouple semantic reasoning\, navigation planning\, and motion control. At the high level\, the structured scene representation summarizes the environment and the VLM provides semantically grounded navigation guidance over it. At the mid level\, a hierarchical room-based navigation strategy reserves VLM guidance for room-level decisions\, which makes effective use of its reasoning while keeping the system efficient. At the low level\, planned waypoints are executed by embodiment-specific motion control. Because only the lowest level depends on the robot\, the same system runs on a custom-built wheeled robot\, the Unitree Go2 quadruped\, and the Unitree G1 humanoid. Across 190 real-world experiments\, it substantially improves success rate and navigates 4-5x more efficiently than existing baselines. To our knowledge\, it is the first system to reliably and efficiently complete floor-scale\, long-range object navigation in complex real-world environments. Together\, these results show that real-world ObjectNav is solved not by a larger model or a single end-to-end policy\, but by a system that balances semantic intelligence with spatial reliability and isolates embodiment-specific control from embodiment-invariant reasoning. \nCommittee:\nJean Oh (advisor)\nJi Zhang\nZhixuan Liu
URL:https://www.ri.cmu.edu/event/from-simulation-to-the-real-world-a-multi-level-system-for-object-navigation-with-vision-language-models/
LOCATION:NSH 3305
CATEGORIES:MSR Thesis Presentation,Student Talks
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DTSTART;TZID=America/New_York:20260911T120000
DTEND;TZID=America/New_York:20260911T130000
DTSTAMP:20260911T184852
CREATED:20260901T200426Z
LAST-MODIFIED:20260901T200426Z
UID:153397-1789128000-1789131600@www.ri.cmu.edu
SUMMARY:Computational Lensing
DESCRIPTION:Abstract: From the cameras in our phones to the lenses in head-mounted displays\, optics shape both how we capture the world and how we experience virtual reality. Most conventional lenses are designed to bring a single plane into focus. In this talk\, we will discuss a new class of computational lens—referred to as a Split-Lohmann lens—that provides spatially varying control over focal length. This capability is achieved by combining a phase-only spatial light modulator with the cubic phase plates used in Lohmann/Alvarez focus-tunable lenses. The resulting computational lens enables new imaging and display capabilities\, including the ability to (i) make a flat display appear to have three-dimensional shape\, and (ii) capture all-in-focus images of highly non-planar scenes.
URL:https://www.ri.cmu.edu/event/computational-lensing/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:Faculty Events
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DTSTART;TZID=America/New_York:20260911T143000
DTEND;TZID=America/New_York:20260911T153000
DTSTAMP:20260911T184852
CREATED:20260828T164708Z
LAST-MODIFIED:20260911T212228Z
UID:153335-1789137000-1789140600@www.ri.cmu.edu
SUMMARY:Augmenting Bee Colonies with Robotics and AI Technologies for Ecosystem Support
DESCRIPTION:Abstract: Earth’s ecosystems are facing a rapid decline in biodiversity\, with honeybees —keystone pollinators critical to ecosystem stability— being among the most affected. The EU-funded RoboRoyale project addresses this crisis by integrating advanced robotics and AI to augment the beehive\, enabling observation at unprecedented resolutions and scales. Featured on the cover of Science Robotics and receiving the 6th Edge of Government Award at the World Government Summit in 2024 our system tracks the Queen’s behaviors\, colony efficiency\, comb states\, and long-term foraging activities\, while advancing micro-robotic intervention capabilities to support hive health. In this talk\, I will discuss the challenges of developing this system\, share key findings regarding complex social interactions\, and explore the future potential of bio-hybrid research. \nBio: Erol Şahin is a Professor of Computer Engineering at Middle East Technical University (METU) and the founding Director of the Center for Robotics and AI (ROMER). Established with over 5 million Euros in funding\, ROMER spans 25\,000 square feet of state-of-the-art facilities\, including prototyping workshops\, specialized research arenas\, and advanced robotic platforms. Dr. Şahin earned his PhD in Cognitive and Neural Systems from Boston University\, following a BSc in Electrical and Electronics Engineering from Bilkent University and an MSc in Computer Engineering from METU. Before assuming his current position\, he worked as postdoctoral researcher at the Université Libre de Bruxelles.   Between 2013 and 2015\, Dr. Sahin spent two years at the Robotics Institute of Carnegie Mellon University during his sabbatical. His research interests include swarm robotics\, robotic learning\, and human-robot interaction—work that has secured more than 2.5 million Euros from the European Union\, TUBITAK\, and industrial partners. Notably\, his contributions to robotic learning were awarded a 53-DOF iCub humanoid platform through the RobotCub project in 2007. Dr. Şahin has edited several journal special issues and books\, currently serves as an Associate Editor for Adaptive Behavior\, and is a member of the Editorial Board for the Swarm Intelligence journal.
URL:https://www.ri.cmu.edu/event/augmenting-bee-colonies-with-robotics-and-ai-technologies-for-ecosystem-support/
LOCATION:1403 Tepper School Building
CATEGORIES:RI Seminar,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ri.cmu.edu/app/uploads/2026/08/erol-sahin.jpg
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