Unveiling the Potential of Vision-Language-Action Models with Open-Ended Multimodal Instructions (2505.11214v1)

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.

Overview of Vision-Language-Action Models with Multimodal Instructions

The paper "Unveiling the Potential of Vision-Language-Action Models with Open-Ended Multimodal Instructions" introduces an advanced concept within Vision-Language-Action (VLA) models aimed at transcending their current limitations—specifically their reliance on language prompts alone. The traditional VLA models have primarily focused on executing robotic tasks through language instructions alone, which limits their scope in interactions that require understanding across multiple modalities such as images and videos. Here, the authors propose OE-VLA, a model capable of processing diverse multimodal instructions, thus broadening its potential applications across varied scenarios of human-robot interaction.

Key Contributions

The OE-VLA model integrates a neural architecture designed to interpret intertwined linguistic and visual data to generate appropriate robotic actions. The paper presents an intuitive method for constructing robotic datasets enhanced with multimodal instructions by refactoring existing language-based datasets and introducing new benchmarks to evaluate performance under diverse task specifications. These benchmarks, OE-CALVIN$_{base}$ and OE-CALVIN$_{hard}$, derive from the CALVIN suite but incorporate varying degrees of complexity in task instructions involving text, images, and video data.

Strong Numerical Results and Experimental Insights

The experimentation results demonstrate that OE-VLA matches, if not exceeds, the performance of conventional VLA models for language instructions while maintaining remarkable accuracy in handling open-ended multimodal tasks. Specifically, the model reported successful sequence lengths averaging 2.99 in a linguistic-only configuration, whereas it averagely scores 3.48 in the OE-CALVIN$_{base}$ benchmark with mixed inputs.

This performance is indicative of its adeptness at not only processing but excelling in tasks where instructions are provided in combined modalities. When faced with challenges in environments featuring multimodal instructions of greater complexity and unfamiliar perspectives, OE-VLA$_{7b}$ continues to exhibit robustness, highlighting how scaling model architectures can enhance interpretation and task execution capabilities.

Technical Details

The innovative adjustment of existing robotic data into multimodal forms includes employing open-source vision-LLMs for object identification, followed by the creation of a database for image-based object representation. The architects of OE-VLA then train the model using a two-stage curriculum learning strategy starting with a multi-image grounding stage followed by fine-tuning on custom datasets specifically structured for open-ended tasks.

Implications and Future Directions

Practically, OE-VLA introduces a paradigm shift in human-robot interaction by allowing robots to comprehend and execute instructions beyond text, thereby increasing operability within diverse, real-world scenarios. The potential applications span everyday tasks to complex operations in uncontrolled environments—expanding implications within fields such as assistive robotics, automated service robots, and exploration systems.

Given the OE-VLA’s promising performance against its benchmarks, further scaling and refinement of architectures, dataset expansion, and exploration of distributional robustness across diverse modalities are intriguing avenues for future research. This work paves the way for integrated AI systems with enhanced adaptability to human inputs beyond the linguistic domain, bringing us closer to the reality of responsive and capable embodied agents characterized by versatile comprehension and interaction capabilities.

In conclusion, the adoption of OE-VLA presents significant advancements in multimodal instruction processing within robotics, setting a robust foundation upon which future research can build toward more generalized, adaptable, and context-aware AI systems in robotics.