⚡️ 假期充电系列继续 今天为大家整理 2025 年 CoRL 期间举办的首届 "Making Sense of Data in Robotics" Workshop，一起探究： 在机器人技术飞速发展的今天，人 们常常把目光聚焦在算法与模型上，是否忽视了真正决定"能否走出实验室、实现大规模落地"的底层变量——数据。 数据不仅是训练基础模型的燃料，更是 支撑策略泛化、稳定运行与安全可控 的地基。没有高质量、场景匹配的数据，再先进的模型也只能停留在论文 与Demo里。 此次Workshop正是一次针对这一"被低估的核心要素"的集体深思。会议聚焦于数据构成、数据筛选与数据可解释性三大命题，试图回答机器人行业最 迫切的问题： 1. 机器人真正需要什么样的数据？ 2. 如何从海量原始信息中提炼出能提升策略表现的数据？ 3. 又该如何理解数据对机器人决策与行为的实际影响？ 锦秋基金（公众号：锦秋集，ID：jqcapital）认为， 这场 Workshop 的价值不只是学术交流，而是揭示了实体智能走向产业化过程中的"关键一 环"。 无论是 Joseph Lim 团队提出的"任务拆解 + 模块复用"式数据高效利用，还是 Ke ...

Core Insights - GenSeg utilizes AI to generate high-quality medical images and corresponding segmentation labels, significantly reducing the manual labeling burden on medical professionals [1][20] - The framework addresses the critical challenge of dependency on large amounts of high-quality annotated data in medical image semantic segmentation [1][20] Summary by Sections Technology Overview - GenSeg is a three-stage framework that tightly couples data augmentation model optimization with semantic segmentation model training, ensuring that generated samples effectively enhance segmentation model performance [2][10] - It can be applied to various segmentation models, such as UNet and DeepLab, improving their performance in both in-domain and out-of-domain scenarios [4][20] Methodology - The framework consists of two main components: a semantic segmentation model that predicts segmentation masks and a mask-to-image generation model that predicts corresponding images [9] - The training process involves three phases: training the generation model with real image-mask pairs, augmenting real segmentation masks to create synthetic image-mask pairs, and evaluating the segmentation model on a validation set to update the generation model [9][10] Experimental Results - GenSeg demonstrates significant sample efficiency, achieving comparable or superior segmentation performance while drastically reducing the number of training samples required [11][20] - In in-domain experiments, GenSeg-UNet requires only 50 images to achieve a Dice score of approximately 0.6, compared to 600 images for standard UNet, representing a 12-fold reduction in data [13] - In out-of-domain tasks, GenSeg-DeepLab achieves a Jaccard index of 0.67 using only 40 images, while standard DeepLab fails to reach this level with 200 images [13] Comparative Analysis - The end-to-end data generation mechanism of GenSeg outperforms traditional separate training strategies, as evidenced by improved performance metrics in various segmentation tasks [15] - Regardless of the type of generation model used, the end-to-end training strategy consistently outperforms the separate training strategy [17] Generalization and Efficiency - GenSeg exhibits strong generalization capabilities across 11 medical image segmentation tasks and 19 datasets, achieving absolute performance improvements of 10-20% while requiring only 1/8 to 1/20 of the training data compared to existing methods [20]

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Chang Nie等 编辑丨具身智能之心 数学表达： 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要 的。 研究背景 机器人模仿学习高度依赖4D多视图序列图像（包含多视角、时间维度的图像），但高质量数据收集成本 高、数量稀缺，严重限制了视觉-语言-动作（VLA）等具身智能策略的泛化与应用。数据增强是缓解数据 稀缺的有效手段，但目前缺乏针对操作任务的4D多视图序列图像编辑方法。 现有方法存在明显的局限：传统数据增强方法（如CACTI、ROSIE）仅针对单张静态图像编辑，无法满足 VLA模型对时空连续4D数据的需求；多视图编辑方法依赖固定相机位置，难以处理机器人操作中动态变化 的多相机系统；视频生成模型因密集时空注意力机制，受限于计算成本，工作窗口小，且难以处理长序列 中的误差累积。 核心挑战与解决方案 ERMV（Editing Robotic Multi-View 4D data）是一种新型数据增强框架，基于单帧 ...