# Proprioception Enhances Vision Language Model in Generating Captions and Subtask Segmentations for Robot Task

arXiv: https://arxiv.org/abs/2512.20876 · HTML: https://mlsanity.com/paper/2512.20876/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 24 of 24)

- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [Learning Universal Policies via Text-Guided Video Generation](https://mlsanity.com/paper/2302.00111/) (2023)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems](https://mlsanity.com/paper/2503.06669/) (2025)
- [Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation](https://mlsanity.com/paper/2312.13139/) (2023)
- [Latent Action Pretraining from Videos](https://mlsanity.com/paper/2410.11758/) (2024)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning](https://mlsanity.com/paper/2302.14115/) (2023)
- [Yell At Your Robot: Improving On-the-Fly from Language Corrections](https://mlsanity.com/paper/2403.12910/) (2024)
- [Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models](https://mlsanity.com/paper/2406.02061/) (2024)
- [The Vector Grounding Problem](https://mlsanity.com/paper/2304.01481/) (2023)
- [From Text to Motion: Grounding GPT-4 in a Humanoid Robot "Alter3"](https://mlsanity.com/paper/2312.06571/) (2023)
- [Language Writ Large: LLMs, ChatGPT, Grounding, Meaning and Understanding](https://mlsanity.com/paper/2402.02243/) (2024)
- [Automatic Generation of Descriptive Titles for Video Clips Using Deep Learning](https://mlsanity.com/paper/2104.03337/) (2021)
- [Deep Predictive Learning: Motion Learning Concept inspired by Cognitive Robotics](https://mlsanity.com/paper/2306.14714/) (2023)
- [Sensorimotor Attention and Language-based Regressions in Shared Latent Variables for Integrating Robot Motion Learning and LLM](https://mlsanity.com/paper/2407.09044/) (2024)
- [Learning Bidirectional Translation between Descriptions and Actions with Small Paired Data](https://mlsanity.com/paper/2203.04218/) (2022)

## Built on by (most cited first, 1 of 1)

- [GeoProp: Grounding Robot State in Vision for Generalist Manipulation](https://mlsanity.com/paper/2607.07101/) (2026)

## Abstract (arXiv)

From the perspective of future developments in robotics, it is crucial to verify whether foundation models trained exclusively on offline data, such as images and language, can understand the robot motion. In particular, since Vision Language Models (VLMs) do not include low-level motion information from robots in their training datasets, video understanding including trajectory information remains a significant challenge. In this study, we assess two capabilities of VLMs through a video captioning task with low-level robot motion information: (1) automatic captioning of robot tasks and (2) segmentation of a series of tasks. Both capabilities are expected to enhance the efficiency of robot imitation learning by linking language and motion and serve as a measure of the foundation model's performance. The proposed method generates multiple "scene" captions using image captions and trajectory data from robot tasks. The full task caption is then generated by summarizing these individual captions. Additionally, the method performs subtask segmentation by comparing the similarity between text embeddings of image captions. In both captioning tasks, the proposed method aims to improve performance by providing the robot's motion data - joint and end-effector states - as input to the VLM. Simulator experiments were conducted to validate the effectiveness of the proposed method.