Benchmarking Vision, Language, & Action Models on Robotic Learning Tasks (2411.05821v2)

Abstract: Vision-language-action (VLA) models represent a promising direction for developing general-purpose robotic systems, demonstrating the ability to combine visual understanding, language comprehension, and action generation. However, systematic evaluation of these models across diverse robotic tasks remains limited. In this work, we present a comprehensive evaluation framework and benchmark suite for assessing VLA models. We profile three state-of-the-art VLM and VLAs - GPT-4o, OpenVLA, and JAT - across 20 diverse datasets from the Open-X-Embodiment collection, evaluating their performance on various manipulation tasks. Our analysis reveals several key insights: 1. current VLA models show significant variation in performance across different tasks and robot platforms, with GPT-4o demonstrating the most consistent performance through sophisticated prompt engineering, 2. all models struggle with complex manipulation tasks requiring multi-step planning, and 3. model performance is notably sensitive to action space characteristics and environmental factors. We release our evaluation framework and findings to facilitate systematic assessment of future VLA models and identify critical areas for improvement in the development of general purpose robotic systems.

• The paper introduces a comprehensive evaluation framework with a novel benchmark suite spanning 20 diverse robotic learning datasets.
• It compares three VLA models—JAT, GPT-4o, and OpenVLA—highlighting significant performance variations, with GPT-4o showing the most consistency.
• The study underscores the need for enhanced platform-agnostic control and refined prompt engineering to improve model generalization in robotics.

Evaluating Vision, Language, and Action Models on Robotic Learning Tasks

The paper "Benchmarking Vision, Language, and Action Models on Robotic Learning Tasks" presents an extensive evaluation framework for assessing the capabilities of sophisticated Vision-Language-Action (VLA) models in the context of robotics learning tasks. Considering the increasing integration of foundational models into robotic frameworks, this research seeks to address the gap in systematic evaluation across varied robotics tasks.

Key Contributions

The paper offers several notable contributions:

• Benchmark Suite: The authors provide a novel benchmark suite designed to evaluate VLA models across 20 datasets within the Open-X-Embodiment collection. This suite focuses on diverse robotic manipulation tasks, unveiling the models' performance variations across different platforms.
• Evaluation Framework: A systematic evaluation framework is introduced, along with a supporting open-source infrastructure, designed to assist the robotics learning community in benchmarking and advancing VLA models.
• Empirical Insights and Analysis: The empirical analysis shows performance discrepancies among current models, with GPT-4o exhibiting the most consistent results. However, the paper emphasizes that model performance significantly depends on both action space characteristics and environmental factors.

Methodology

The evaluation employs the Open X-Embodiment Dataset, a comprehensive collection of real-world robotic trajectories, to provide a diverse basis for assessing generalist models. The selection of 53 datasets, utilized in the presented analysis, spans over 1 million trajectories covering diverse manipulation and locomotion tasks, which enables an extensive analysis of model performance.

Model Evaluation and Results

Three VLA models are evaluated:

1. JAT (Jack of All Trades): A transformer-based model noted for its dual attention mechanism, which handles long-horizon tasks through sequence processing.
2. GPT-4o: Known for its omni-modal processing abilities, enabling it to manage text, audio, image, and video inputs effectively. The model's ability to generalize across various environments is a point of focus.
3. OpenVLA: An open-source, large-scale model optimized for multi-task environments with robust language grounding capabilities.

The evaluation metrics primarily use Mean Squared Error (MSE) to determine prediction accuracy, supplemented by various normalized error measures. The empirical findings suggest:

• GPT-4o demonstrates more consistent normalized performance due to sophisticated prompt engineering strategies.
• OpenVLA performs strongly in scenarios within its training distribution but shows variation in more complex tasks.
• JAT generally displays higher error margins, possibly suggesting architectural limitations concerning precision-demanding tasks.

Implications and Future Directions

The paper underscores several critical areas for development in VLA models, notably the necessity of enhancing platform-agnostic control capacities. Moreover, the pronounced disparity in performance across different task types implies a significant scope for architectural advancements, particularly in addressing robustness against diverse control scenarios. The emphasis on sophisticated prompt engineering, as evidenced by GPT-4o's results, indicates that detailed task and action space information could enhance the performance and generalization of VLA models.

The future work section outlines plans to further expand the benchmark to cover more diverse datasets and evaluate multi-modal learning capabilities. The ambition to transition from offline observations to online evaluations will provide more dynamic insights into the real-time decision-making efficacies of these models.

Conclusion

This paper offers a robust foundation for benchmark evaluations of current VLA models across robotic learning tasks, providing crucial insights into model capabilities and limitations. The framework and methodologies laid out herein will be pertinent for fostering continued advancements in deploying VLA capabilities in practical and dynamic robotics applications. Through rigorous evaluation and transparency, this work aims to catalyze progress in developing more comprehensive, multi-faceted generalist robotic models.