4 papers
VLM-GLoc: Vision-Language Model Enhanced Monte Carlo Localization for Robust Semantic Global Localization in Cluttered Quasi-Static Environments
Shivendra Agrawal, Bradley Hayes
Global localization in geometrically aliased, quasi-static environments such as grocery stores, offices, schools, and hospitals poses a significant challenge for mobile robots. Gro…
GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology
Shivendra Agrawal, Bradley Hayes
Navigating complex, densely packed environments like retail stores, warehouses, and hospitals poses a significant spatial grounding challenge for humans and embodied AI. In these s…
ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors
Shivendra Agrawal, Jake Brawer, Ashutosh Naik +2
Many indoor workspaces are quasi-static: their global geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and percep…
ShelfHelp: Empowering Humans to Perform Vision-Independent Manipulation Tasks with a Socially Assistive Robotic Cane
Shivendra Agrawal, Suresh Nayak, Ashutosh Naik +1
The ability to shop independently, especially in grocery stores, is important for maintaining a high quality of life. This can be particularly challenging for people with visual im…