7 papers · 1 filter
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
Jadelynn Dao, Milan Ganai, Yasmina Abukhadra +7
Vision-Language Models (VLMs) are increasingly deployed as high-level planners for embodied agents, with an emerging strategy of scaling test-time compute to improve capability. Ho…
Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees
Jakob Thumm, Marian Frei, Tianle Ni +2
We propose a framework for vision-based human pose estimation and motion prediction that gives conformal prediction guarantees for certifiably safe human-robot collaboration. Our f…
Sim2Val: Leveraging Correlation Across Test Platforms for Variance-Reduced Metric Estimation
Rachel Luo, Heng Yang, Michael Watson +4
Learning-based robotic systems demand rigorous validation to assure reliable performance, but extensive real-world testing is often prohibitively expensive, and if conducted may st…
Performance assessment of ADAS in a representative subset of critical traffic situations
Luigi Di Lillo, Andrea Triscari, Xilin Zhou +3
As a variety of automated collision prevention systems gain presence within personal vehicles, rating and differentiating the automated safety performance of car models has become…
Diagnostic Runtime Monitoring with Martingales
Ali Hindy, Rachel Luo, Somrita Banerjee +3
Machine learning systems deployed in safety-critical robotics settings must be robust to distribution shifts. However, system designers must understand the cause of a distribution…
Real-Time Anomaly Detection and Reactive Planning with Large Language Models
Rohan Sinha, Amine Elhafsi, Christopher Agia +3
Foundation models, e.g., large language models (LLMs), trained on internet-scale data possess zero-shot generalization capabilities that make them a promising technology towards de…