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X-WR-CALNAME:Robotics Institute Carnegie Mellon University
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X-WR-CALDESC:Events for Robotics Institute Carnegie Mellon University
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DTSTART;TZID=America/New_York:20260715T110000
DTEND;TZID=America/New_York:20260715T120000
DTSTAMP:20260725T222827
CREATED:20260710T161451Z
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SUMMARY:MapForest: A Modular Field Robotics System for Forest Mapping
DESCRIPTION:Abstract:\nForests present compounding challenges for mobile mapping systems. Dense canopy degrades GNSS\, uneven terrain demands deployment across diverse platforms\, and no single sensing platform can capture the full vertical structure of a forest — from the canopy above to the understory below. Yet precise\, georeferenced maps of individual trees are exactly what ecologists and forest managers need to monitor invasive species\, estimate biomass\, and track ecosystem change at scale. \nThis talk presents MapForest\, a modular field robotics system that turns multi-modal sensor data (LiDAR\, IMU\, GNSS\, and RGB imagery) into georeferenced 3D reconstructions and GIS-ready outputs. A single compact payload deploys across five carriers (handheld\, bicycle\, ATV\, and two UAVs) without hardware modification\, enabling seamless data collection across heterogeneous terrain. \nThe core mapping pipeline extends a LiDAR-inertial SLAM framework with covariance-aware GNSS priors and a Huber robust loss\, reducing trajectory error by 67% relative to a GNSS-blind baseline. To bridge the viewpoint gap between aerial and ground surveys\, we develop two complementary aerial-terrestrial alignment methods: Tensor-MI\, an analytical mutual-information approach operating on tree-likelihood fields\, and CRAF\, a learned registration model combining modality-specific encoders with a cross-attention transformer. As a concrete ecological application\, MapForest localizes invasive Tree-of-Heaven from onboard imagery using a fine-tuned detector\, projecting detections into the 3D map and exporting georeferenced GeoTIFF layers suitable for direct use in forestry workflows. \nMapForest is evaluated across six field sites spanning approximately 30 km of multi-modal traversal data\, demonstrating that robust\, actionable forest inventory is achievable at a granularity unavailable to satellite or conventional aerial methods. \nThesis Committee:\nProf. Abhisesh Silwal (chair)\nProf. Michael Kaess\nJohn Kim
URL:https://www.ri.cmu.edu/event/mapforest-a-modular-field-robotics-system-for-forest-mapping/
LOCATION:GHC 4405
CATEGORIES:MSR Thesis Presentation,Student Talks
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DTSTART;TZID=America/New_York:20260715T130000
DTEND;TZID=America/New_York:20260715T140000
DTSTAMP:20260725T222827
CREATED:20260716T132559Z
LAST-MODIFIED:20260716T132559Z
UID:152106-1784120400-1784124000@www.ri.cmu.edu
SUMMARY:Knowledge Graph-Augmented Reinforcement Learning: Injecting Structured Task Knowledge into Arbitrary Policy Architectures
DESCRIPTION:Abstract:\nReinforcement learning agents in complex tasks often require extensive exploration of large state spaces before useful structure emerges. Instead of pure exploration for learning tasks\, it is possible to leverage high-level semantic knowledge such as recipes\, instructions\, or labels\, and adapt to new tasks by grounding that prior knowledge in the environment. This talk presents Knowledge Graph-Augmented Reinforcement Learning (KG-RL)\, a method that augments RL policies with structured graph knowledge and injects it into a variety of policy networks across a variety of environments. \nThe method takes the form of a plug-in adapter. A task knowledge graph is merged each step with a scene graph\, encoded by a Graph Convolutional Recurrent Network\, and pooled through a small recommender into a fixed-width feature vector concatenated with the policy backbone’s observation features. The backbone itself is untouched\, so the adapter slots into learned policies as varied as CNN-MLP\, SoftMoE-LSTM\, GTrXL\, and PoliFormer without modification; bringing it to a new environment requires only enumerating a handful of entities and relation templates. The knowledge graphs are instantiated against simulator-exposed tables in symbolic environments\, or mined by a one-time pre-pass of the same perception pipeline that builds per-step scene graphs in open-world scenarios. \nWe evaluate the adapter on four environments (Overcooked-AI\, MiniGrid\, Craftax\, and AI-Habitat ObjectNav) and four backbones. It delivers two improvements independent of backbone choice: faster training to the same final policy\, reaching the same asymptotic reward in up to 3x fewer environment steps\, and a higher final policy under a fixed budget\, where on Craftax and AI-Habitat the method overtakes the available published baselines. Both gains scale with task complexity\, and the adapter is robust to substantial knowledge-graph corruption\, retaining its advantage with half of the graph’s nodes removed. These results argue that structured task knowledge belongs as a default\, low-cost input channel in modern reinforcement learning. \nCommittee:\nKatia Sycara (advisor)\nChangliu Liu\nRenos Zabounidis
URL:https://www.ri.cmu.edu/event/knowledge-graph-augmented-reinforcement-learning-injecting-structured-task-knowledge-into-arbitrary-policy-architectures/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:MSR Thesis Presentation,Student Talks
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DTSTART;TZID=America/New_York:20260715T133000
DTEND;TZID=America/New_York:20260715T150000
DTSTAMP:20260725T222827
CREATED:20260709T134546Z
LAST-MODIFIED:20260709T143200Z
UID:151750-1784122200-1784127600@www.ri.cmu.edu
SUMMARY:Tracing Generated Content Back to Training Data
DESCRIPTION:Abstract: \nAI-generated content is inherently derived from training data\, yet it remains a mystery which specific data points large generative models rely on for a given generation. To address this\, my research focuses on training data attribution—identifying the training images that are most influential in synthesizing a specific output. The ideal objective is to find the exact subset of training data that\, if removed\, would prevent a retrained model from generating that content. However\, the required combinatorial search and iterative retraining are computationally intractable. \nTo make this search tractable\, we propose using model unlearning as a proxy for the counterfactual model. By forcing a model to unlearn a synthesized output\, we can trace which training data points it effectively “removes” by observing changes in the training loss. We show that this is a highly effective method for finding influential data in text-to-image models. \nNext\, we interpret these attribution results by identifying which features best predict them. We show that joint text-image features\, after light calibration\, serve as strong predictors of attribution. We then analyze whether textual or visual features are more salient in the attribution process. We find that this salience shifts drastically across different settings. \nTo enable more fine-grained analysis of the attribution results\, we introduce TPIPS\, a similarity metric conditioned on specific visual aspects. We repurpose a vision-language model to compute text-conditioned\, aspect-specific perceptual similarity grounded in human judgments. \nBased on the analysis\, I will conclude by discussing the next open challenges and potential next steps in the field. \nThesis Committee Members: \nJun-Yan Zhu\, Chair \nDeva Ramanan \nRuslan Salakhutdinov \nAlexei A. Efros\, UC Berkeley \nDavid Bau\, Northeastern
URL:https://www.ri.cmu.edu/event/tracing-generated-content-back-to-training-data/
LOCATION:Newell-Simon Hall 4305
CATEGORIES:PhD Thesis Defense,Student Talks
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