1 citations · 1 across the 5 of their papers we have counts for
6 papers
TrafficSignBench: Rule-Centric Closed-Loop Evaluation of Traffic-Sign Compliance in Autonomous Driving
Victoria Smirnova, Viktoriia Zinkovich, Gregorii Bukhtuev +4
Autonomous driving planners are typically evaluated using aggregate metrics such as driving score, destination rate, and collision rate, which do not explicitly measure compliance…
Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models
Nikita Kachaev, Andrey Moskalenko, Matvey Skripkin +10
Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual kno…
BREPS: Bounding-Box Robustness Evaluation of Promptable Segmentation
Andrey Moskalenko, Danil Kuznetsov, Irina Dudko +6
Promptable segmentation models such as SAM have established a powerful paradigm, enabling strong generalization to unseen objects and domains with minimal user input, including poi…
RoboBenchMart: Benchmarking Robots in Retail Environment
Konstantin Soshin, Alexander Krapukhin, Andrei Spiridonov +4
Most existing robotic manipulation benchmarks focus on tabletop or household scenarios. While these setups have driven impressive progress, it remains unclear whether generalist VL…
SPARTA: Evaluating Reasoning Segmentation Robustness through Black-Box Adversarial Paraphrasing in Text Autoencoder Latent Space
Viktoriia Zinkovich, Anton Antonov, Andrei Spiridonov +6
Multimodal large language models (MLLMs) have shown impressive capabilities in vision-language tasks such as reasoning segmentation, where models generate segmentation masks based…
Bring the Apple, Not the Sofa: Impact of Irrelevant Context in Embodied AI Commands on VLA Models
Daria Pugacheva, Andrey Moskalenko, Denis Shepelev +3
Vision Language Action (VLA) models are widely used in Embodied AI, enabling robots to interpret and execute language instructions. However, their robustness to natural language va…