2 citations · 2 across the 10 of their papers we have counts for
11 papers
Careful Judge: Safe and Efficient Human-AI Collaborative Decision Making
Chenyu Zhang, Rachel Luo, Boyi Li +3
In human-AI collaborative decision making, human review can prevent unsafe AI decisions, but each human judgment is costly. Treating human intervention after AI abstention as a one…
Coverage Aware Active Evaluation for Failure Discovery with Paired Systems
Anjali Parashar, Rachel Luo, Apoorva Sharma +6
Autonomous systems can fail in rare and heterogeneous ways, making real-world failure discovery difficult under limited testing budgets. Although cheaper proxies such as simulators…
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
Safe human-robot collaboration (HRC) requires accurate human pose estimation and motion prediction to prevent critical collisions. Existing certifiable safe HRC approaches are high…
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…
Realistic Extreme Behavior Generation for Improved AV Testing
Robert Dyro, Matthew Foutter, Ruolin Li +4
This work introduces a framework to diagnose the strengths and shortcomings of Autonomous Vehicle (AV) collision avoidance technology with synthetic yet realistic potential collisi…
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…
ZAPP! Zonotope Agreement of Prediction and Planning for Continuous-Time Collision Avoidance with Discrete-Time Dynamics
Luca Paparusso, Shreyas Kousik, Edward Schmerling +2
The past few years have seen immense progress on two fronts that are critical to safe, widespread mobile robot deployment: predicting uncertain motion of multiple agents, and plann…
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition
Roberto Bigazzi, Lorenzo Baraldi, Shreyas Kousik +2
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments. In the literature, there has been an extensive f…