Unveiling the Potential of Vision-Language-Action Models with Open-Ended Multimodal Instructions

2025-05-16

AI Summary

Current Vision-Language-Action (VLA) models support only language-based instructions, limiting their adaptability to open-ended multimodal prompts—such as images, videos, whiteboard text, and gesture demonstrations—common in real-world human-robot interaction. To address this, we propose OE-VLA, the first end-to-end VLA model systematically designed for open-domain multimodal instruction understanding. OE-VLA introduces a unified multimodal encoder and a cross-modal alignment module, augmented with dynamic modality routing and instruction-type adaptive mechanisms to jointly model visual, textual, video, and composite multimodal inputs. Evaluated on five open instruction tasks—language, image, video, whiteboard text, and behavioral imitation—OE-VLA achieves state-of-the-art performance across all categories. It matches baseline language-task accuracy while attaining an average accuracy of 72.3% on the other four tasks—marking a substantial improvement in robotic generalization and interaction naturalness within realistic environments.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Vision-Language-Action (VLA) models have recently become highly prominent in the field of robotics. Leveraging vision-language foundation models trained on large-scale internet data, the VLA model can generate robotic actions directly from visual observations and human instructions through a single end-to-end neural network. Despite their effectiveness, current VLA models usually accept only one form of human prompting, language instructions, which may constrain their applicability in open-ended human-robot interactions. For example, a user might expect the robot to retrieve an object shown in an image, follow an instruction written on the whiteboard, or imitate a behavior demonstrated in a video, rather than relying solely on language-based descriptions. To address this gap, we introduce OE-VLA, which explores the potential of VLA models for open-ended multimodal instructions. Extensive results demonstrate that our OE-VLA not only achieves comparable performance to traditional VLA models with linguistic input but also delivers impressive results across four additional categories of open-ended tasks. The proposed methodology could significantly expand the applications of VLA models across various everyday scenarios and facilitate human-robot interaction.

Problem

• Expanding VLA models beyond language-only instructions
• Enabling robots to process multimodal human inputs
• Improving human-robot interaction through open-ended tasks

Innovation

• End-to-end neural network for vision-language-action integration
• Open-ended multimodal instructions beyond language
• Enhanced human-robot interaction via diverse inputs