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Confusion-Aware In-Context-Learning for Vision-Language Models in Robotic Manipulation

Yayun He, Zuheng Kang, Botao Zhao, Zhouyin Wu, Junqing Peng, Jianzong Wang

Abstract

Vision-language models (VLMs) have significantly improved the generalization capabilities of robotic manipulation. However, VLM-based systems often suffer from a lack of robustness, leading to unpredictable errors, particularly in scenarios involving confusable objects. Our preliminary analysis reveals that these failures are mainly caused by shortcut learning problem inherently in VLMs, limiting their ability to accurately distinguish between confusable features. To this end, we propose Confusion-Aware In-Context Learning (CAICL), a method that enhances VLM performance in confusable scenarios for robotic manipulation. The approach begins with confusion localization and analysis, identifying potential sources of confusion. This information is then used as a prompt for the VLM to focus on features most likely to cause misidentification. Extensive experiments on the VIMA-Bench show that CAICL effectively addresses the shortcut learning issue, achieving a 85.5\% success rate and showing good stability across tasks with different degrees of generalization.

Confusion-Aware In-Context-Learning for Vision-Language Models in Robotic Manipulation

Abstract

Vision-language models (VLMs) have significantly improved the generalization capabilities of robotic manipulation. However, VLM-based systems often suffer from a lack of robustness, leading to unpredictable errors, particularly in scenarios involving confusable objects. Our preliminary analysis reveals that these failures are mainly caused by shortcut learning problem inherently in VLMs, limiting their ability to accurately distinguish between confusable features. To this end, we propose Confusion-Aware In-Context Learning (CAICL), a method that enhances VLM performance in confusable scenarios for robotic manipulation. The approach begins with confusion localization and analysis, identifying potential sources of confusion. This information is then used as a prompt for the VLM to focus on features most likely to cause misidentification. Extensive experiments on the VIMA-Bench show that CAICL effectively addresses the shortcut learning issue, achieving a 85.5\% success rate and showing good stability across tasks with different degrees of generalization.
Paper Structure (19 sections, 5 figures, 2 tables)

This paper contains 19 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. VLMs for Robotic Manipulation
  4. Shortcut Learning Problem
  5. Methodology
  6. Shortcut Learning Problem in VLMs For Robotic Manipulation
  7. Confusion-Aware In-Context Learning
  8. Confusion Localization and Analysis
  9. Confusion-Aware Image Recognition
  10. Experiments
  11. Experimental Setup
  12. Baselines
  13. Learning-based Methods
  14. Prompt-based Methods
  15. Results
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Illustration of Shortcut Learning in Vision-Language Models (VLM) and the Confusion-Aware In-Context Learning (CAICL) Approach. The upper part shows the VLM making incorrect decisions due to confusion between similar objects, a result of shortcut learning. The proposed CAICL method, however, guides the VLM to scan the entire scene, identify confusable features, and improve image recognition accuracy.
  • Figure 2: The overall process of the confusion-aware in-context-learning (CAICL).
  • Figure 3: Prompts used for CAICL in confusion location and image recognition.
  • Figure 4: The left subplot shows the proportion of the failure cases on the VIMA-Bench Task 17. The right plot illustrates the proportion of the confusion cases and confusion cases for the incorrect image recognition cases.
  • Figure 5: Some selected tasks of VIMA-Bench.