arXiv AI
Jun 16

Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time

arXiv:2606. 15631v1 Announce Type: cross Abstract: Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute.

By Jeongeun Park, Juhan Park, Taekyung Kim, Sungjoon Choi, Dongyoon Han, Sangdoo Yun
arXiv Computer Vision
Aug 27

Moving Beyond More Views: Redundancy-Aware Ego-Exo Fusion for Proficiency Estimation

The paper introduces a redundancy-aware fusion framework for EgoExo proficiency estimation, which integrates fine-grained motion cues from egocentric views with spatial context from exocentric views. It identifies multiview redundancy and overfitting as key challenges and proposes two modules—AdaMVS for adaptive view selection and VIB-GB for compressing redundant signals—to address them. Experiments on EgoExo-4D and EgoExo-Fitness show that the method learns to select informative views and fuse them effectively, achieving state‑of‑the‑art results.

By Xu Dong, Wanqing Li, Anthony Adeyemi-Ejeye, Andrew Gilbert
arXiv AI
Jun 9

AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing

arXiv:2606. 09811v1 Announce Type: cross Abstract: World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning.

By Jisong Cai, Long Ling, Shiwei Chu, Zhongshan Liu, Jiayue Kang, Zhixuan Liang, Wenjie Xu, Yinan Mao, Weinan Zhang, Xiaokang Yang, Ru Ying, Ran Zheng, Yao Mu
arXiv Computer Vision
Sep 21

MT-WAM: Reorienting the One-Pass Predictive Representation Toward Action Generation

MT‑WAM enhances the Fast‑WAM framework by adding complementary supervision for future 2‑D point trajectories and visual features while keeping the original training objectives. A lightweight dual‑stream branch and structured attention mask isolate motion‑specific processing, and motion‑stream tokens provide additional dynamics cues to the action expert. During inference, MT‑WAM skips future‑video prediction, using cached video and motion information to achieve higher success rates on LIBERO, LIBERO‑Plus, RoboTwin 2.0 Clean2Rand, and several real‑world tasks.

By Yiguang Yang, Jiankun Peng, Xiaoming Wang, Yiran Zhang, Zhibo Fang