51 citations · 196 across the 44 of their papers we have counts for
35 papers · 1 filter
X4Val: Learning Neural Surrogates for Variance-Reduced Policy Evaluation
Rachel Luo, Michael Watson, Apoorva Sharma +6
Rigorous evaluation of learning-based robotic systems is an essential prerequisite for deployment. However, real-world test data is expensive to gather; moreover, in a typical iter…
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
NVIDIA, :, Yan Wang +41
End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tai…
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…
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…