Papers with OpenVLA
ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)
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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu
| Challenge: | Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data . |
| Approach: | They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference. |
| Outcome: | The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets. |
VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making (2025.emnlp-main)
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| Challenge: | Recent large-scale pretrained models are built upon a multi-input single-output paradigm . tasks compete for a shared output channel, creating mutual exclusion effects . |
| Approach: | They propose a multi-input single-output (MISO) paradigm for large pretrained models . they propose unified training framework that enables concurrent multi-task outputs . |
| Outcome: | Experiments on autonomous driving platform show that MIMO-VLA outperforms state-of-the-art models in MIMO settings. |
ElasticFlow: One-Step Physics-Consistent Policy with Elastic Time Horizons for Language-Guided Manipulation (2026.findings-acl)
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| Challenge: | Existing methods for embodied AI use iterative denoising to achieve high latency and lack physical consistency. |
| Approach: | They propose a distillation-free, physics-consistent one-step policy framework that reconstructs the Mean Field Theory by directly modeling the average velocity field. |
| Outcome: | Experiments on LIBERO, CALVIN, and RoboTwin show that the proposed framework outperforms state-of-the-art methods on long-horizon tasks. |