PosA-VLA: Enhancing Action Generation via Pose-Conditioned Anchor Attention
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908698028277760 |
|---|---|
| author | Li, Ziwen Wang, Xin Zhang, Hanlue Chen, Runnan Lin, Runqi He, Xiao Huang, Han Guo, Yandong Karray, Fakhri Liu, Tongliang Gong, Mingming |
| author_facet | Li, Ziwen Wang, Xin Zhang, Hanlue Chen, Runnan Lin, Runqi He, Xiao Huang, Han Guo, Yandong Karray, Fakhri Liu, Tongliang Gong, Mingming |
| contents | The Vision-Language-Action (VLA) models have demonstrated remarkable performance on embodied tasks and shown promising potential for real-world applications. However, current VLAs still struggle to produce consistent and precise target-oriented actions, as they often generate redundant or unstable motions along trajectories, limiting their applicability in time-sensitive scenarios.In this work, we attribute these redundant actions to the spatially uniform perception field of existing VLAs, which causes them to be distracted by target-irrelevant objects, especially in complex environments.To address this issue, we propose an efficient PosA-VLA framework that anchors visual attention via pose-conditioned supervision, consistently guiding the model's perception toward task-relevant regions. The pose-conditioned anchor attention mechanism enables the model to better align instruction semantics with actionable visual cues, thereby improving action generation precision and efficiency. Moreover, our framework adopts a lightweight architecture and requires no auxiliary perception modules (e.g., segmentation or grounding networks), ensuring efficient inference. Extensive experiments verify that our method executes embodied tasks with precise and time-efficient behavior across diverse robotic manipulation benchmarks and shows robust generalization in a variety of challenging environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03724 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PosA-VLA: Enhancing Action Generation via Pose-Conditioned Anchor Attention Li, Ziwen Wang, Xin Zhang, Hanlue Chen, Runnan Lin, Runqi He, Xiao Huang, Han Guo, Yandong Karray, Fakhri Liu, Tongliang Gong, Mingming Computer Vision and Pattern Recognition Robotics The Vision-Language-Action (VLA) models have demonstrated remarkable performance on embodied tasks and shown promising potential for real-world applications. However, current VLAs still struggle to produce consistent and precise target-oriented actions, as they often generate redundant or unstable motions along trajectories, limiting their applicability in time-sensitive scenarios.In this work, we attribute these redundant actions to the spatially uniform perception field of existing VLAs, which causes them to be distracted by target-irrelevant objects, especially in complex environments.To address this issue, we propose an efficient PosA-VLA framework that anchors visual attention via pose-conditioned supervision, consistently guiding the model's perception toward task-relevant regions. The pose-conditioned anchor attention mechanism enables the model to better align instruction semantics with actionable visual cues, thereby improving action generation precision and efficiency. Moreover, our framework adopts a lightweight architecture and requires no auxiliary perception modules (e.g., segmentation or grounding networks), ensuring efficient inference. Extensive experiments verify that our method executes embodied tasks with precise and time-efficient behavior across diverse robotic manipulation benchmarks and shows robust generalization in a variety of challenging environments. |
| title | PosA-VLA: Enhancing Action Generation via Pose-Conditioned Anchor Attention |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2512.03724 |