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MuBlE|机器人操作数据集|仿真环境数据集
收藏arXiv2025-03-05 更新2025-03-06 收录
下载链接：
https://github.com/michaal94/MuBlE
下载链接
链接失效反馈资源简介：
MuBlE是由华为诺亚方舟实验室和布拉格捷克技术大学等机构开发的一款基于MuJoCo物理引擎和Blender渲染器的仿真环境，专为长距离机器人操作任务设计。该数据集包含12000个场景，涵盖十种单步和多项步骤的桌面操作任务，支持视觉、语言和操作的闭合循环方法。它能够生成高质量的多模态数据，并包含SHOP-VRB2这一新基准，该基准包含需要同时进行视觉和物理测量的多步骤推理场景。
提供机构：
华为诺亚方舟实验室
创建时间：
2025-03-05
AI搜集汇总
数据集介绍
构建方式
MuBlE数据集的构建基于Robosuite框架，融合了MuJoCo物理引擎和Blender渲染器。MuJoCo负责提供逼真的物理模拟，确保物体间的交互符合物理规律；Blender则负责生成高质量的图像，为视觉识别提供真实感。MuBlE支持多模态数据生成，包括场景和指令的合成、物体属性的地面实况场景图生成、任务完成评估以及一系列基本动作控制器，这些控制器能够观测物理物体的属性，如重量、硬度等。此外，MuBlE的高效物理和关键帧动作渲染循环促进了闭环推理方法的集成。
使用方法
MuBlE数据集的使用方法包括场景生成、指令生成、动作规划和执行等。首先，使用场景生成器工具创建各种场景，并随机放置对象。然后，指令生成器根据场景生成自然语言指令。接着，在动作规划阶段，MuBlE根据视觉输出和基本动作输入与推理方法进行交互。最后，在物理循环中，MuJoCo物理引擎根据控制信号进行物理交互计算，生成一系列观察数据，如末端执行器的位置和方向、夹持器状态、重量测量值等。MuBlE数据集可以用于训练和评估机器人操作任务，支持闭环推理和任务规划，是研究机器人操作任务的重要资源。
背景与挑战
背景概述
MuBlE数据集是一项开创性的研究，旨在解决当前机器人操作体在执行长时任务时面临的挑战。这些任务需要机器人与物理世界进行交互，以获取必要的信息，例如按重量对物体进行分类。为了提高此类机器人的能力，研究人员需要相关训练环境。因此，MuBlE应运而生，这是一个基于robosuite的模拟环境，利用MuJoCo物理引擎和高质量的渲染器Blender，提供逼真的视觉观察，同时确保物理状态的准确性。MuBlE是第一个专注于长时机器人操作任务的模拟器，保留了准确的物理建模。此外，MuBlE能够生成多模态数据用于训练，并通过环境交互在两个级别上设计闭环方法：视觉 - 动作循环，和控制 - 物理循环。
当前挑战
MuBlE数据集面临的挑战主要包括：1) 解决的领域问题，即机器人操作体在执行长时任务时需要与物理世界进行交互以获取必要信息，这要求模拟环境能够提供逼真的视觉观察和准确的物理建模；2) 构建过程中所遇到的挑战，包括如何在保持高质量渲染的同时，实现实时物理计算，以及如何设计能够支持闭环推理方法的模拟器。
常用场景
经典使用场景
MuBlE数据集是一款专为机器人操控任务规划而设计的模拟环境，它利用MuJoCo物理引擎和高质量渲染器Blender，提供了既真实又准确的视觉观测数据。该数据集的经典使用场景包括长时机器人操控任务，如将物体按重量从轻到重排序，这些任务需要机器人与物理世界进行交互以获取必要的信息。MuBlE数据集通过提供多模态数据，为训练和设计闭环方法提供了基础，支持视觉-动作循环和控制-物理循环两个层面的环境交互。
解决学术问题
MuBlE数据集解决了当前具身推理代理在规划长时机器人操控任务方面的困难，这类任务需要与物理世界进行交互以获取必要的信息。该数据集通过提供一个既真实又准确的物理建模，使得机器人可以更好地规划动作，并执行长时任务。此外，MuBlE数据集还提供了一个新的基准测试SHOP-VRB2，该测试由10类多步骤推理场景组成，需要同时进行视觉和物理测量，为评估具身、闭环推理在机器人操控中的能力提供了新的挑战。
实际应用
MuBlE数据集在实际应用中，可以用于机器人操控任务的训练和评估。例如，可以利用MuBlE数据集进行机器人操控任务的模拟训练，然后通过SHOP-VRB2基准测试评估机器人的操控能力。此外，MuBlE数据集还可以用于研究机器人与物理世界的交互，以及如何通过操控世界来获取关于非视觉物体属性的知识。
数据集最近研究
最新研究方向
在机器人操作任务规划领域，MuBlE数据集的最新研究方向聚焦于构建能够模拟长时机器人操作任务的真实物理环境。该数据集利用MuJoCo物理引擎和高质量的渲染器Blender，为机器人操作任务提供了既真实又准确的视觉观察数据。MuBlE环境通过视觉-动作循环和控制-物理循环两个层面的环境交互，支持闭环方法的设计，并为训练和评估机器人操作任务规划提供了多模态数据。此外，MuBlE还提出了SHOP-VRB2基准，包含10个多步推理场景类，要求同时进行视觉和物理测量，从而推动了机器人操作任务规划领域的长时闭环推理研究。MuBlE数据集的研究对于提高机器人操作任务规划的能力，尤其是在处理需要与物理世界进行物理交互的长时任务方面，具有重要意义。
相关研究论文
- 1MuBlE: MuJoCo and Blender simulation Environment and Benchmark for Task Planning in Robot Manipulation华为诺亚方舟实验室 · 2025年
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