13 papers
ECHO: Embodied Camera Observations of Human Object Carrying
Xuefei Sun, Lorin Achey, Kali Hamilton +4
Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment and the habits of the people wh…
Belief Consistency Between Foundation-Model Evidence and Geometric Perception in Persistent Robotic Maps
Christoffer Heckman, Harel Biggie, Brendan Crowe +1
Persistent maps used by autonomous robots increasingly fuse a geometric perception stack whose assertions are well-characterized with a foundation-model channel that produces seman…
SceneGraphGrounder: Zero-Shot 3D Visual Grounding via Structured Scene Graph Matching
Xuefei Sun, Xujia Zhang, Brendan Crowe +2
Zero-shot 3D visual grounding requires localizing objects in unstructured environments from free-form natural language. Recent vision-language model (VLM) approaches achieve promis…
Weather-Robust Scene Semantics with Vision-Aligned 4D Radar
Kali Hamilton, Christoffer Heckman
Cameras and LiDAR degrade in rain, fog, and snow, while millimeter-wave radar remains largely unaffected. We align a radar encoder to frozen SigLIP vision embeddings and decode str…
RF-Modulated Adaptive Communication Improves Multi-Agent Robotic Exploration
Lorin Achey, Breanne Crockett, Christoffer Heckman +1
Reliable coordination and efficient communication are critical challenges for multi-agent robotic exploration of environments where communication is limited. This work introduces A…
Reducing Text Bias in Synthetically Generated MCQAs for VLMs in Autonomous Driving
Sutej Kulgod, Sean Ye, Sanchit Tanwar +1
Multiple Choice Question Answering (MCQA) benchmarks are an established standard for measuring Vision Language Model (VLM) performance in driving tasks. However, we observe the kno…
Octree Diffusion for Semantic Scene Generation and Completion
Xujia Zhang, Brendan Crowe, Christoffer Heckman
The completion, extension, and generation of 3D semantic scenes are an interrelated set of capabilities that are useful for robotic navigation and exploration. Existing approaches…
Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis
Lorin Achey, Alec Reed, Brendan Crowe +2
We present a novel approach for enhancing robotic exploration by using generative occupancy mapping. We implement SceneSense, a diffusion model designed and trained for predicting…
Spatial-LLaVA: Enhancing Large Language Models with Spatial Referring Expressions for Visual Understanding
Xuefei Sun, Doncey Albin, Cecilia Mauceri +2
Multimodal large language models (MLLMs) have demonstrated remarkable abilities in comprehending visual input alongside text input. Typically, these models are trained on extensive…
Foundation Models for Rapid Autonomy Validation
Alec Farid, Peter Schleede, Aaron Huang +1
We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it co…
Online Diffusion-Based 3D Occupancy Prediction at the Frontier with Probabilistic Map Reconciliation
Alec Reed, Lorin Achey, Brendan Crowe +2
Autonomous navigation and exploration in unmapped environments remains a significant challenge in robotics due to the difficulty robots face in making commonsense inference of unob…
Radar-Based Localization For Autonomous Ground Vehicles In Suburban Neighborhoods
Andrew J. Kramer, Christoffer Heckman
For autonomous ground vehicles (AGVs) deployed in suburban neighborhoods and other human-centric environments the problem of localization remains a fundamental challenge. There are…
SceneSense: Diffusion Models for 3D Occupancy Synthesis from Partial Observation
Alec Reed, Brendan Crowe, Doncey Albin +3
When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. When entering space that was previously obs…