Selective Perception for Robot: Task-Aware Attention in Multimodal VLA

• URL: http://arxiv.org/abs/2602.15543v1
• Date: Tue, 17 Feb 2026 12:48:59 GMT
• Title: Selective Perception for Robot: Task-Aware Attention in Multimodal VLA
• Abstract summary: Vision-Language-Action (VLA) models integrate diverse multimodal signals from multi-view inputs.<n>Inspired by the principles of human active perception, we propose a dynamic information fusion framework.
• Score: 6.550868784168723
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In robotics, Vision-Language-Action (VLA) models that integrate diverse multimodal signals from multi-view inputs have emerged as an effective approach. However, most prior work adopts static fusion that processes all visual inputs uniformly, which incurs unnecessary computational overhead and allows task-irrelevant background information to act as noise. Inspired by the principles of human active perception, we propose a dynamic information fusion framework designed to maximize the efficiency and robustness of VLA models. Our approach introduces a lightweight adaptive routing architecture that analyzes the current text prompt and observations from a wrist-mounted camera in real-time to predict the task-relevance of multiple camera views. By conditionally attenuating computations for views with low informational utility and selectively providing only essential visual features to the policy network, Our framework achieves computation efficiency proportional to task relevance. Furthermore, to efficiently secure large-scale annotation data for router training, we established an automated labeling pipeline utilizing Vision-Language Models (VLMs) to minimize data collection and annotation costs. Experimental results in real-world robotic manipulation scenarios demonstrate that the proposed approach achieves significant improvements in both inference efficiency and control performance compared to existing VLA models, validating the effectiveness and practicality of dynamic information fusion in resource-constrained, real-time robot control environments.

Related papers

• Afford-VLA: Action-Aligned Visual Planning via Internalized Affordance [108.46436073194546]
  We argue that effective planning should be local, visually grounded, internally generated, and directly aligned with action.<n>We propose Afford-VLA, a unified framework that internalizes task-conditioned affordance as an explicit visual planning interface within VLA models.
  arXiv  Detail & Related papers  (2026-05-22T20:43:47Z)
• VISion On Request: Enhanced VLLM efficiency with sparse, dynamically selected, vision-language interactions [51.41587958253802]
  We introduce VISion On Request (VISOR), a method that reduces inference cost without discarding visual information.<n>VISOR improves efficiency by sparsifying the interaction between image and text tokens.<n>Experiments show that VISOR drastically reduces computational cost while matching or exceeding state-of-the-art results.
  arXiv  Detail & Related papers  (2026-03-24T17:58:17Z)
• Co-Training Vision Language Models for Remote Sensing Multi-task Learning [68.15604397741753]
  Vision language models (VLMs) have achieved promising results in RS image understanding, grounding, and ultra-high-resolution (UHR) image reasoning.<n>We present RSCoVLM, a simple yet flexible VLM baseline for RS MTL.<n>We propose a unified dynamic-resolution strategy to address the diverse image scales inherent in RS imagery.
  arXiv  Detail & Related papers  (2025-11-26T10:55:07Z)
• PhysVLM-AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments [36.84821207878773]
  Visual reasoning in multimodal large language models (MLLMs) has primarily been studied in static, fully observable settings.<n>We introduce the Active Visual Reasoning (AVR) task, extending visual reasoning to partially observable, interactive environments.<n>We present a benchmark featuring multi-round interactive environments designed to assess both reasoning and information-gathering efficiency.
  arXiv  Detail & Related papers  (2025-10-24T02:59:00Z)
• Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization [61.55616421408666]
  Low-Altitude Economy Networks (LAENets) have enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.<n> onboard vision (VLMs) offer inference for real-time inference but limited onboard dynamic network conditions.<n>We propose a UAV-enabled LAENet system that improves communication efficiency under dynamic LAENet conditions.
  arXiv  Detail & Related papers  (2025-10-11T05:11:21Z)
• dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought [66.78110237549087]
  Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics.<n>We introduce dVLA, a diffusion-based VLA that unifies visual perception, language reasoning, and robotic control in a single system.
  arXiv  Detail & Related papers  (2025-09-30T02:36:11Z)
• TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies [95.30717188630432]
  We introduce visual trace prompting to facilitate VLA models' spatial-temporal awareness for action prediction.<n>We develop a new TraceVLA model by finetuning OpenVLA on our own collected dataset of 150K robot manipulation trajectories.<n>We present a compact VLA model based on 4B Phi-3-Vision, pretrained on the Open-X-Embodiment and finetuned on our dataset.
  arXiv  Detail & Related papers  (2024-12-13T18:40:51Z)
• ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning [38.26304604660713]
  ADEM-VL is an efficient vision-language method that tunes models based on pretrained large language models. Our framework surpasses existing methods by an average accuracy of 0.77% on ScienceQA dataset.
  arXiv  Detail & Related papers  (2024-10-23T11:31:06Z)
• Flex: End-to-End Text-Instructed Visual Navigation from Foundation Model Features [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies.<n>Our findings are synthesized in Flex (Fly lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors.<n>We demonstrate the effectiveness of this approach on a quadrotor fly-to-target task, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• Bridging Language, Vision and Action: Multimodal VAEs in Robotic Manipulation Tasks [0.0]
  In this work, we focus on unsupervised vision-language--action mapping in the area of robotic manipulation.<n>We propose a model-invariant training alternative that improves the models' performance in a simulator by up to 55%.<n>Our work thus also sheds light on the potential benefits and limitations of using the current multimodal VAEs for unsupervised learning of robotic motion trajectories.
  arXiv  Detail & Related papers  (2024-04-02T13:25:16Z)
• Expanding Frozen Vision-Language Models without Retraining: Towards Improved Robot Perception [0.0]
  Vision-language models (VLMs) have shown powerful capabilities in visual question answering and reasoning tasks. In this paper, we demonstrate a method of aligning the embedding spaces of different modalities to the vision embedding space. We show that using multiple modalities as input improves the VLM's scene understanding and enhances its overall performance in various tasks.
  arXiv  Detail & Related papers  (2023-08-31T06:53:55Z)
• Multi-Task Variational Information Bottleneck [8.55293326934818]
  Multi-task learning (MTL) is an important subject in machine learning and artificial intelligence. This article proposes an MTL model based on the architecture of the variational information bottleneck (VIB) Extensive observations on three public data sets under adversarial attacks show that the proposed model is competitive to the state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-07-01T09:06:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.