arXiv AI
Sep 30

FineART: Fine-grained Annotated Robotic Trajectory Dataset and Vision-Language-Action Model for Bimanual Manipulation

arXiv:2609.36416v1 Announce Type: cross Abstract: Robots operating in real-world environments must execute complex, multi-step bimanual tasks over long horizons rather than single, isolated actions....

By Jade Choghari, Pepijn Kooijmans, Mansi Agarwal, Yusuf Umut Ciftci, Aseem Doriwala, Catherine Weaver, Mouli Sivapurapu, Kai Yang, Jackson Lee, Thomas Wolf, Pragna Mannam
arXiv AI
Oct 2

FineART: Fine-Grained Annotated Robotic Trajectory Dataset and Vision-Language-Action Model for Bimanual Manipulation

arXiv:2609.36416v2 Announce Type: replace-cross Abstract: Robots operating in real-world environments must often execute complex, multi-step bimanual tasks over long horizons rather than single, isol...

By Jade Choghari, Pepijn Kooijmans, Mansi Agarwal, Yusuf Umut Ciftci, Aseem Doriwala, Catherine Weaver, Mouli Sivapurapu, Kai Yang, Thomas Wolf, Jackson Lee, Pragna Mannam
arXiv Machine Learning
Oct 9

When Listening Becomes Easier: Scrubbing Visual Cues for Shortcut-Free VLAs

The paper addresses shortcut learning in vision‑language‑action (VLA) models, showing that different backbones vary in their susceptibility to visual shortcuts that arise from limited demonstration diversity. It introduces a representation‑level metric called action margin to predict shortcut use without rollouts, and proposes task scrubbing—a domain‑adversarial training method—to reduce reliance on visual cues and enhance out‑of‑distribution robustness. Experiments in simulation and real‑world settings demonstrate that task scrubbing improves generalization and often eliminates visual shortcut learning.

By Jasper Gerigk, Kenzo Aspuru-Takata, Chin-Hsuan Wu, Mohammad Mohammadi, Shuhong Zheng, Igor Gilitschenski