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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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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:20260918T100000
DTEND;TZID=America/New_York:20260918T113000
DTSTAMP:20260911T185410
CREATED:20260908T155758Z
LAST-MODIFIED:20260908T155947Z
UID:153494-1789725600-1789731000@www.ri.cmu.edu
SUMMARY:RI PhD Thesis Defense - Ingrid Navarro Anaya
DESCRIPTION:Date: September 18th\, 2026 \nTime: 10:00 AM (ET) \nZoom: link \nLocation: NSH 4305 \nType: PhD Thesis Defense \nWho: Ingrid Navarro Anaya \n  \nTitle: Towards Generalizable Motion Prediction under Distribution Shifts \n  \nAbstract: \nAutonomous robots are increasingly expected to operate in dynamic\, human-centered environments. To do so safely and efficiently\, they must reason about how people move and interact. In domains like driving\, social navigation\, and aviation\, a common approach is to learn models of human motion directly from recorded data and use the resulting priors to inform downstream systems like simulators and planning stacks. \n  \nDespite the growing availability of datasets\, benchmarks\, and modeling techniques\, state-of-the-art methods remain unreliable for real-world deployment\, often generalizing poorly to novel environments and rare events. Much of this stems from recorded datasets covering few environments and few safety-relevant events relative to what a deployed system will ultimately encounter. Broadly\, the field has addressed this in four main ways: validating autonomy stacks on the road\, collecting more data\, synthesizing relevant scenarios\, and adapting at test time. These are all valuable and necessary strategies\, but each is bounded by risk\, cost\, the sim-to-real gap\, or the difficulty of reliably detecting and adapting to a shift\, respectively. \n  \nThese strategies share the premise that the data we hold is insufficient. This dissertation argues that such data is also underexploited and thus pursues a complementary direction\, asking how much actionable signal existing datasets already contain but current practice overlooks. We do so through a recurring paradigm we call scenario characterization: describing a scenario in terms of a property of interest and acting on that description downstream. We use this paradigm in three ways. The first is for guidance and abstraction\, shaping what a model trains or optimizes. The second is for mining and benchmarking\, determining what a model trains on and what is withheld to test it. The third is for analysis\, fixing the basis on which results are studied. We apply these uses across two settings: in-distribution generalization\, which draws mainly on guidance and abstraction\, and generalization under distribution shift\, which draws on all three and is where the main contributions concentrate. \n  \nWe further argue that evidence for generalization is typically gathered only within a single domain\, so a claim that holds there is rarely challenged elsewhere. This is largely because\, outside autonomous driving\, no motion domain offers comparable infrastructure and scale. This dissertation therefore contributes aviation as a testbed\, introducing a large-scale framework and dataset for airport surface movement forecasting. \n  \nThrough this framework and our experimental settings\, we expose generalization failures that would otherwise have remained hidden in aggregate metrics. We also enable reading aviation and driving on a common basis\, hinting at what transfers and what does not. Finally\, we also provide early evidence that the signal recovered through our framework may help systems gain the risk awareness needed to handle real-world critical events. \n  \nThesis Committee: \nJean Oh (co-chair) \nJonathan Francis (co-chair) \nSebastian Scherer \nAndrea Bajcsy \nAlexandre Alahi (EPFL)
URL:https://www.ri.cmu.edu/event/ri-phd-thesis-defense-ingrid-navarro-anaya/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Defense,Student Talks
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