10 Open Challenges Steering the Future of Vision-Language-Action Models

• URL: http://arxiv.org/abs/2511.05936v1
• Date: Sat, 08 Nov 2025 09:02:13 GMT
• Title: 10 Open Challenges Steering the Future of Vision-Language-Action Models
• Authors: Soujanya Poria, Navonil Majumder, Chia-Yu Hung, Amir Ali Bagherzadeh, Chuan Li, Kenneth Kwok, Ziwei Wang, Cheston Tan, Jiajun Wu, David Hsu,
• Abstract summary: Vision-language-action (VLA) models are increasingly prevalent in the embodied AI arena.<n>We discuss 10 principal milestones in the ongoing development of VLA models.
• Score: 57.817832960995354
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Due to their ability of follow natural language instructions, vision-language-action (VLA) models are increasingly prevalent in the embodied AI arena, following the widespread success of their precursors -- LLMs and VLMs. In this paper, we discuss 10 principal milestones in the ongoing development of VLA models -- multimodality, reasoning, data, evaluation, cross-robot action generalization, efficiency, whole-body coordination, safety, agents, and coordination with humans. Furthermore, we discuss the emerging trends of using spatial understanding, modeling world dynamics, post training, and data synthesis -- all aiming to reach these milestones. Through these discussions, we hope to bring attention to the research avenues that may accelerate the development of VLA models into wider acceptability.

Related papers

• Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning [124.48672228625821]
  We introduce Vlaser - a Vision-Language-Action Model with synergistic embodied reasoning capability.<n>Vlaser achieves state-of-the-art performance across a range of embodied reasoning benchmarks.<n>Our approach achieves state-of-the-art results on the WidowX benchmark and competitive performance on the Google Robot benchmark.
  arXiv  Detail & Related papers  (2025-10-13T05:51:22Z)
• Can World Models Benefit VLMs for World Dynamics? [59.73433292793044]
  We investigate the capabilities when world model priors are transferred into Vision-Language Models.<n>We name our best-performing variant Dynamic Vision Aligner (DyVA)<n>We find DyVA to surpass both open-source and proprietary baselines, achieving state-of-the-art or comparable performance.
  arXiv  Detail & Related papers  (2025-10-01T13:07:05Z)
• Pure Vision Language Action (VLA) Models: A Comprehensive Survey [16.014856048038272]
  The emergence of Vision Language Action (VLA) models marks a paradigm shift from traditional policy-based control to generalized robotics.<n>This survey delves into advanced VLA methods, aiming to provide a clear taxonomy and a systematic, comprehensive review of existing research.
  arXiv  Detail & Related papers  (2025-09-23T13:53:52Z)
• Survey of Vision-Language-Action Models for Embodied Manipulation [12.586030711502858]
  Embodied intelligence systems enhance agent capabilities through continuous environment interactions.<n>Vision-Language-Action models, inspired by advancements in large foundation models, serve as universal robotic control frameworks.<n>This survey comprehensively reviews VLA models for embodied manipulation.
  arXiv  Detail & Related papers  (2025-08-21T03:30:04Z)
• DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge [41.030494146004806]
  We propose DreamVLA, a novel VLA framework that integrates comprehensive world knowledge forecasting to enable inverse dynamics modeling.<n>DreamVLA introduces a dynamic-region-guided world knowledge prediction, integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning.<n>Experiments on both real-world and simulation environments demonstrate that DreamVLA achieves 76.7% success rate on real robot tasks.
  arXiv  Detail & Related papers  (2025-07-06T16:14:29Z)
• Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends [11.678954304546988]
  Vision-language-action (VLA) models extend vision-language models (VLM)<n>This paper reviews post-training strategies for VLA models through the lens of human motor learning.
  arXiv  Detail & Related papers  (2025-06-26T03:06:57Z)
• Unified Vision-Language-Action Model [86.68814779303429]
  We present UniVLA, a unified and native multimodal VLA model that autoregressively models vision, language, and action signals as discrete token sequences.<n>Our approach sets new state-of-the-art results across several widely used simulation benchmarks, including CALVIN, LIBERO, and Simplenv-Bridge.<n>We further demonstrate its broad applicability on real-world ALOHA manipulation and autonomous driving.
  arXiv  Detail & Related papers  (2025-06-24T17:59:57Z)
• Continual Learning for Generative AI: From LLMs to MLLMs and Beyond [56.29231194002407]
  We present a comprehensive survey of continual learning methods for mainstream generative AI models.<n>We categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based.<n>We analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones.
  arXiv  Detail & Related papers  (2025-06-16T02:27:25Z)
• Vision-Language-Action Models: Concepts, Progress, Applications and Challenges [4.180065442680541]
  Vision-Language-Action models aim to unify perception, natural language understanding, and embodied action within a single computational framework.<n>This foundational review presents a comprehensive synthesis of recent advancements in Vision-Language-Action models.<n>Key progress areas include architectural innovations, parameter-efficient training strategies, and real-time inference accelerations.
  arXiv  Detail & Related papers  (2025-05-07T19:46:43Z)
• OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving [12.004183122121042]
  OccLLaMA is an occupancy-language-action generative world model. We build a unified multi-modal vocabulary for vision, language and action. OccLLaMA achieves competitive performance across multiple tasks.
  arXiv  Detail & Related papers  (2024-09-05T06:30:01Z)
• A Survey on Vision-Language-Action Models for Embodied AI [90.99896086619854]
  Embodied AI is widely recognized as a key element of artificial general intelligence.<n>A new category of multimodal models has emerged to address language-conditioned robotic tasks in embodied AI.<n>We present the first survey on vision-language-action models for embodied AI.
  arXiv  Detail & Related papers  (2024-05-23T01:43:54Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)

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.