7 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
SABRE: Scalable and Automated Benchmarking of VLMs under Stress
Zixuan Lan, Luzhe Sun, Matthew R. Walter +1
Vision-language models (VLMs) are improving rapidly, but benchmark development lags behind, making weaknesses hard to identify. Building stress tests is costly: samples must satisf…
Flow-based Policy Adaptation without Policy Updates
Luzhe Sun, Jingtian Ji, Haoran Chen +2
Foundation models and other policies trained on large and diverse datasets can endow robots with semantic and task knowledge that would be difficult to acquire from task-specific d…
What Are We Actually Benchmarking in Robot Manipulation?
Tianchong Jiang, Xiangshan Tan, Samuel Wheeler +3
A robotics benchmark score measures success under one fixed evaluation setup, yet is routinely treated as evidence of general manipulation capability. We identify four failure mode…
Seeing without Looking: Do Vision-Language Benchmarks Really Test Vision?
Zixuan Lan, Luzhe Sun, Matthew R. Walter +1
Benchmark accuracy is often implicitly assumed to reflect grounded visual understanding in vision-language models (VLMs), yet it remains unclear to what extent such scores truly re…
HALP: Detecting Hallucinations in Vision-Language Models without Generating a Single Token
Sai Akhil Kogilathota, Sripadha Vallabha E G, Luzhe Sun +1
Hallucinations remain a persistent challenge for vision-language models (VLMs), which often describe nonexistent objects or fabricate facts. Existing detection methods typically op…