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TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics

Created by
  • Haebom

Author

Yi Han, Cheng Chi, Enshen Zhou, Shanyu Rong, Jingkun An, Pengwei Wang, Zhongyuan Wang, Lu Sheng, Shanghang Zhang

Outline

To overcome the limitations of Vision-Language Models (VLMs), we propose the Tool-Integrated Geometric Reasoning (TIGeR) framework. TIGeR enables VLMs to generate and execute precise geometric computations via external tools, thereby exceeding the limitations of conventional pattern recognition approaches and providing the precision required for real-world robotics. TIGeR recognizes geometric reasoning requirements, synthesizes appropriate computational code, and invokes specialized libraries. Using the TIGeR-300K dataset and a two-stage training pipeline, we achieve state-of-the-art performance on geometric reasoning benchmarks and demonstrate centimeter-level precision in real-world robotic manipulation tasks.

Takeaways, Limitations

Takeaways:
Enhancing the geometric reasoning capabilities of VLMs to enhance their practical applications in robotics.
Perform complex geometric calculations accurately using external tools.
Presenting practical robotic manipulation possibilities with centimeter-level precision.
Achieving SOTA with the TIGeR-300K dataset and a two-stage training pipeline.
Limitations:
Lack of information about the specific model structure and types of tools.
Potential vulnerability to changes in computational speed and external environment due to dependence on external tools.
Possible limitations in generalization performance due to the size and diversity of the training dataset.
Further research is needed to validate the performance of TIGeR in complex environments.
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