MultiNet: A Generalist Benchmark for Multimodal Action models

A Benchmark for the Action Age

Multinet is a comprehensive benchmark designed to evaluate and advance the development of multimodal action models—AI systems capable of processing visual input, understanding language, and generating appropriate actions. Our benchmark addresses a critical research gap by providing standardized evaluation frameworks that assess both the action capabilities of Vision-Language Models (VLMs) and the multimodal understanding of Vision-Language-Action Models (VLAs).

Key contributions of Multinet include:

• The largest open-source generalist dataset, consolidating diverse modalities and tasks suitable for pre-training, fine-tuning, and evaluation of multimodal action models
• A unified benchmark for systematically assessing state-of-the-art models across vision-language association, language understanding, and control tasks
• Open-source tools for standardizing complex reinforcement learning and robotics data from various sources
• A framework for mapping VLMs to other modality classes, with particular emphasis on action spaces
• Comprehensive evaluation protocols for profiling SoTA VLMs and VLAs on benchmark datasets

The Importance of Multinet

Recent advances in machine learning have demonstrated the potential of large-scale models to exhibit broad generalization capabilities across diverse tasks. Emerging Vision-Language-Action (VLA) models such as OpenVLA, Pi0, and Pi0 with FAST demonstrate impressive capabilities in taking vision-language-grounded actions in control tasks. Yet, these models are typically specialized for a narrow set of robotics tasks, and their performance on broader vision-language understanding remains underexplored.

Multinet addresses these limitations by:

• Advancing Generalist Foundation Models: Providing a comprehensive benchmark to evaluate truly generalist models across multiple modalities and environments
• Enabling Next-Generation VLAs: Offering pre-training scale data across modalities to develop models that achieve state-of-the-art performance across all constituent tasks
• Standardizing Evaluation: Creating unified assessment protocols for robotics foundation models beyond narrow control tasks
• Democratizing Access: Releasing open-source tools that address challenges of outdated formats, poor maintenance, and accessibility issues in robotics and RL datasets

Through our initial release and latest work, we've demonstrated significant capability gaps in current models when handling out-of-distribution tasks. These findings highlight the need for more versatile generalist architectures and provide a foundation for developing the next generation of truly generalist AI systems.

Dataset Coverage and Analysis

Multinet offers a rich collection of diverse datasets to develop comprehensive multimodal capabilities across multiple domains. It can be utilized to train models that will cultivate sophisticated vision-language association knowledge, high quality language understanding and generation abilities, and prowess in reward-based action trajectories in a myriad of environments and tasks. This consolidation effort not only provides pre-training scale data for a generalist objective, but also serves as a valuable benchmark for evaluating the capabilities of current SoTA VLMs and VLAs, illuminating paths toward more versatile and capable generalist AI systems.

Explore Multinet Datasets

Benchmark

While existing benchmarks excel at evaluating specific capabilities and modalities, there remains a notable gap in holistic evaluation frameworks that can assess both the action capabilities of Vision-Language Models (VLMs) and the multimodal understanding of Vision-Language-Action Models (VLAs). Multinet addresses this gap by providing a comprehensive benchmark that spans vision-language, language, RL, and Robotics tasks. Our work consolidates diverse, high-quality datasets and establishes standardized evaluation metrics to enable systematic comparison of state-of-the-art models. For a detailed list of datasets and metrics included in Multinet, please refer to our dataset specification paper

Explore Benchmark Metrics & Categories

Metrics in Multinet benchmark and the categories of tasks they each evaluate

What's Next

The release of Multinet is a preliminary but importantstep toward next-generation AI systems, with ambitious research directions planned for future iterations:

• Comprehensive Modality Analysis: Systematically evaluate how control-task training affects vision-language capabilities in VLAs. This investigation will provide crucial insights into architectural trade-offs when developing truly generalist models.
• Expanded Evaluation Horizons: Integrate diverse control tasks beyond OpenX-Embodiment and Procgen to thoroughly assess generalization to completely unseen environments. This will rigorously identify architectural bottlenecks and generalization boundaries.
• Advanced Transfer Learning: Extend beyond zero-shot performance to explore few-shot learning and fine-tuning paradigms, with particular emphasis on transfer to novel domains like software environments. This research will advance our understanding of learning transferable representations across embodied and digital tasks.
• Simulation-Based Online Evaluation: Transform Multinet from its current offline form into an interactive online benchmark leveraging state-of-the-art simulation environments. We're developing world models to power these simulations for both 2D and 3D control tasks, enabling dynamic assessment of model capabilities in responsive environments.
• Multi-Domain Expertise: Research cross-domain adaptation mechanisms that allow models to seamlessly operate across diverse environmental contexts, from robotics to software agents and game environments.

Through these research directions, we aim to establish Multinet as a comprehensive platform advancing generalist AI research. By developing sophisticated evaluation environments and methodologies, we will accelerate progress toward more versatile and robust artificial intelligence systems capable of generalizing across an unprecedented range of tasks and domains.