ManipBench: Benchmarking Vision-LLMs for Low-Level Robot Manipulation
The paper "ManipBench: Benchmarking Vision-LLMs for Low-Level Robot Manipulation" addresses a pivotal challenge in the intersection of vision-LLMs (VLMs) and robotics. VLMs, characterized by their commonsense reasoning capabilities, have predominantly been utilized for high-level planning applications in robotics. However, their utility in precise, low-level robot manipulation remains less explored, largely owing to the absence of benchmarks tailored for such tasks. The authors propose a benchmark, ManipBench, aimed at evaluating VLMs' ability to assist in low-level robotic manipulation, thus filling a critical gap in current research.
Key Contributions and Methodology:
- Benchmark Structure: ManipBench is an open-source benchmark that utilizes multiple-choice questions to assess VLMs' understanding of low-level robotic manipulation. It targets several dimensions, including object-object interactions and deformable object manipulation, without requiring trajectory rollouts. This method offers an efficient evaluation of reasoning capabilities by VLMs.
- Evaluation Dataset: The benchmark includes 12,617 questions derived from a diverse set of sources—existing public datasets, a custom in-house fabric manipulation setup, and simulation environments. This diversity ensures a comprehensive evaluation across tasks like pick-and-place, articulated object manipulation, and dynamic manipulation.
- Model Evaluation: The authors extensively test 33 representative VLMs across 10 model families, including both open-source and closed-source models like GPT-4 and Gemini. This evaluation highlights significant performance variability across tasks and identifies models such as Gemini-2.5-pro as top performers. The findings emphasize a considerable gap between VLM capabilities and human-level understanding in manipulation tasks.
- Real-World Correlation: The paper demonstrates a strong correlation between model performance on ManipBench and their real-world effectiveness, thereby validating the benchmark's utility as a proxy for evaluating VLMs in embodied robotic settings.
- Implications and Future Directions: The findings reveal that while the best-performing models can outperform random chance significantly, there remains substantial room for improvement in VLMs' reasoning capabilities in robotic manipulation. This highlights the need for continued innovation in model development.
Practical and Theoretical Implications:
The introduction of ManipBench has profound implications for both the development and evaluation of VLMs in robotics. Practically, it provides a framework for systematically assessing the effectiveness of various VLMs, guiding the selection of models for specific robotic manipulation tasks. Theoretically, the benchmark's insights into model performance variability offer valuable lessons for refining model architectures and training approaches.
Furthermore, the benchmark's design encourages a focus on low-level reasoning capabilities, vital for tasks requiring precise manipulation, such as deformable object handling—a challenging area that remains underexplored compared to rigid object manipulation. As the field progresses, leveraging VLMs to bridge the sim-to-real gap in robotics presents a promising avenue for research. ManipBench sets a foundational stepping stone in this journey.
In conclusion, the paper marks a significant contribution to robotics and artificial intelligence by addressing the evaluation gap for VLMs in low-level manipulation tasks. The benchmark not only provides a comprehensive tool for assessing current model capabilities but also encourages future advancements that push the boundary towards generalist robotic solutions.