Title: Interleave-VLA: Enhancing Robot Manipulation with Image-Text Interleaved Instructions
Primary area: applications to robotics, autonomy, planning
Keywords: Robot Learning, Foundation Model, Multimodal Learning

Abstract: The rise of foundation models paves the way for generalist robot policies in the physical world. Existing methods relying on text-only instructions often struggle to generalize to unseen scenarios. We argue that interleaved image-text inputs offer richer and less biased context and enable robots to better handle unseen tasks with more versatile human-robot interaction.
Building on this insight, Interleave-VLA, the first robot learning paradigm capable of comprehending interleaved image-text instructions and directly generating continuous action sequences in the physical world, is introduced.
It offers a natural, flexible, and model-agnostic paradigm that extends state-of-the-art vision-language-action (VLA) models with minimal modifications while achieving strong zero-shot generalization.
Interleave-VLA also includes an automatic pipeline that converts text instructions from Open X-Embodiment into interleaved image-text instructions, resulting in a large-scale real-world interleaved embodied dataset with 210k episodes.
Comprehensive evaluation in simulation and the real world shows that Interleave-VLA offers two major benefits: (1) improves out-of-domain generalization to unseen objects by 2× compared to text input baselines, (2) supports flexible task interfaces and diverse instructions in a zero-shot manner, such as hand-drawn sketches.
We attribute Interleave-VLA's strong zero-shot capability to the use of instruction images, which effectively mitigate hallucinations, and the inclusion of heterogeneous multimodal datasets, enriched with Internet-sourced images, offering potential for scalability. [Our project site](https://interleave-vla.github.io/Interleave-VLA-Anonymous/) has more information.

TL;DR: We introduce a vision-language-action paradigm that understands interleaved image-text instructions and generates continuous actions, improving zero-shot generalization in real-world robotics with a large-scale interleaved embodied dataset.