构建轮椅与机械臂协同控制的仿真系统，生成可用于联合学习的数据集。
WheelArm-Sim: A Manipulation and Navigation Combined Multimodal Synthetic Data Generation Simulator for Unified Control in Assistive Robotics
- 在Isaac Sim中构建轮椅-机械臂一体化仿真平台，生成多模态数据。
- 采集13项任务、232条轨迹、67,783个样本的综合数据集。
- 适用于需要轮椅与机械臂协同控制的辅助机器人研究者。
轮椅和机械臂能帮助上肢及行动能力受限人群完成日常生活活动（ADL）。尽管近期研究分别聚焦于轮椅-安装机械臂（WMRAs）和轮椅本身，但利用机器学习模型实现二者集成统一控制仍鲜有探索。为此，我们提出轮椅-机械臂（WheelArm）概念，即融合轮椅与机械臂控制的集成式人机系统。数据收集是构建此类系统的第一步。本文介绍基于Isaac Sim开发的WheelArm-Sim仿真框架，用于合成数据采集。通过该框架，我们构建了一个包含13项任务、232条轨迹、67,783个样本的操纵与导航联合多模态数据集。为验证数据潜力，我们在芥末采摘任务中实现基线动作预测模型，结果表明，来自WheelArm-Sim的数据可有效支持数据驱动的集成控制学习。
原文摘要 · Abstract (English)
Wheelchairs and robotic arms enhance independent living by assisting individuals with upper-body and mobility limitations in their activities of daily living (ADLs). Although recent advancements in assistive robotics have focused on Wheelchair-Mounted Robotic Arms (WMRAs) and wheelchairs separately, integrated and unified control of the combination using machine learning models remains largely underexplored. To fill this gap, we introduce the concept of WheelArm, an integrated cyber-physical system (CPS) that combines wheelchair and robotic arm controls. Data collection is the first step toward developing WheelArm models. In this paper, we present WheelArm-Sim, a simulation framework developed in Isaac Sim for synthetic data collection. We evaluate its capability by collecting a manipulation and navigation combined multimodal dataset, comprising 13 tasks, 232 trajectories, and 67,783 samples. To demonstrate the potential of the WheelArm dataset, we implement a baseline model for action prediction in the mustard-picking task. The results illustrate that data collected from WheelArm-Sim is feasible for a data-driven machine learning model for integrated control.
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