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

• URL: http://arxiv.org/abs/2603.15134v1
• Date: Mon, 16 Mar 2026 11:26:45 GMT
• Title: Confusion-Aware In-Context-Learning for Vision-Language Models in Robotic Manipulation
• Abstract summary: Vision-language models (VLMs) have significantly improved the generalization capabilities of robotic manipulation.<n>These systems often suffer from a lack of robustness, leading to unpredictable errors.<n>We propose Confusion-Aware In-Context Learning (CAICL) to enhance VLM performance in confusable scenarios.
• Score: 25.784453718723423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Two Bridges, One Pathway: From VLMs to Generalizable VLAs with Embodied Trajectory-Coupled Data [93.7685703383343]
  Vision-language models (VLMs) are powerful general-purpose reasoners, yet converting them into robot control policies is surprisingly difficult.<n>We argue that this gap can be bridged gradually with the right intermediate data.<n>We show that mixing task-relevant out-of-distribution ETC data with a small amount of action data enables the model to generalize to novel visual-language conditions.
  arXiv  Detail & Related papers  (2026-06-07T08:57:51Z)
• Seeing Clearly, Reasoning Confidently: Plug-and-Play Remedies for Vision Language Model Blindness [36.09698262750699]
  We introduce an efficient plug-and-play module that substantially improves vision language models' reasoning over rare objects.<n>We learn multi-modal class embeddings for rare objects by leveraging prior knowledge from vision foundation models and synonym-augmented text descriptions.<n> Experiments on two benchmarks show consistent and substantial gains for pretrained VLMs in rare object recognition and reasoning.
  arXiv  Detail & Related papers  (2026-02-23T09:02:40Z)
• Cross-Modal Attention Guided Unlearning in Vision-Language Models [16.460281156521646]
  Vision-Language Models (VLMs) have demonstrated immense capabilities in multi-modal understanding and inference tasks.<n>VLMs add a layer of complexity to this process, as the visual context in the query may also contain sensitive information in addition to the text.<n>We formulate Cross-Modal Attention Guided Unlearning (CAGUL), a lightweight and efficient VLM unlearning framework.
  arXiv  Detail & Related papers  (2025-10-08T21:21:59Z)
• Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting [70.83781268763215]
  Vision-language models (VLMs) have achieved impressive performance across diverse multimodal tasks by leveraging large-scale pre-training.<n>VLMs face unique challenges such as cross-modal feature drift, parameter interference due to shared architectures, and zero-shot capability erosion.<n>This survey aims to serve as a comprehensive and diagnostic reference for researchers developing lifelong vision-language systems.
  arXiv  Detail & Related papers  (2025-08-06T09:03:10Z)
• Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities [54.94982467313341]
  Vision-language Models (VLMs) have emerged as general-purpose tools for addressing a variety of complex computer vision problems.<n>We set out to understand the limitations of SoTA VLMs on fundamental visual tasks by constructing a series of tests that probe which components of design, specifically, may be lacking.
  arXiv  Detail & Related papers  (2025-07-10T15:26:41Z)
• Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation [90.00687889213991]
  Solving complex long-horizon robotic manipulation problems requires sophisticated high-level planning capabilities.<n>Vision-language models (VLMs) pretrained on Internet data could in principle offer a framework for tackling such problems.<n>In this paper, we introduce a novel test-time framework that enhancesVLMs' physical reasoning capabilities for multi-stage manipulation tasks.
  arXiv  Detail & Related papers  (2025-02-23T20:42:15Z)
• Response Wide Shut: Surprising Observations in Basic Vision Language Model Capabilities [30.176918208200604]
  Vision-Language Models (VLMs) have emerged as general purpose tools for addressing a variety of complex computer vision problems. These models have been shown to be highly capable, but also lacking some basic visual understanding skills. This paper sets out to understand the limitations of SoTA VLMs on fundamental visual tasks.
  arXiv  Detail & Related papers  (2024-08-13T08:26:32Z)
• MOKA: Open-World Robotic Manipulation through Mark-Based Visual Prompting [97.52388851329667]
  We introduce Marking Open-world Keypoint Affordances (MOKA) to solve robotic manipulation tasks specified by free-form language instructions. Central to our approach is a compact point-based representation of affordance, which bridges the VLM's predictions on observed images and the robot's actions in the physical world. We evaluate and analyze MOKA's performance on various table-top manipulation tasks including tool use, deformable body manipulation, and object rearrangement.
  arXiv  Detail & Related papers  (2024-03-05T18:08:45Z)
• Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning [67.0609518552321]
  We propose to conduct Machine Vision Therapy which aims to rectify the noisy predictions from vision models. By fine-tuning with the denoised labels, the learning model performance can be boosted in an unsupervised manner.
  arXiv  Detail & Related papers  (2023-12-05T07:29:14Z)
• ViCor: Bridging Visual Understanding and Commonsense Reasoning with Large Language Models [27.5219975853389]
  We find that pre-trained vision-and-language models (VLMs) and large language models (LLMs) are good at different kinds of visual commonsense reasoning problems. For problems where the goal is to infer conclusions beyond image content,VLMs face difficulties, while LLMs, given sufficient visual evidence, can use commonsense to infer the answer well.
  arXiv  Detail & Related papers  (2023-10-09T17:10:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.