5 papers
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…
Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
Alexander Tyurin, Andrei Spiridonov, Varvara Rudenko
We study distributed reinforcement learning (RL) with policy gradient methods under asynchronous and parallel computations and communications. While non-distributed methods are wel…
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…
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…
Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation
Nikita Kachaev, Andrei Spiridonov, Andrey Gorodetsky +8
Benchmarks are crucial for evaluating progress in robotics and embodied AI. However, a significant gap exists between benchmarks designed for high-level language instruction follow…