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.

翻译：轮椅与机械臂通过辅助上肢功能受限及行动不便人士的日常生活活动（ADL），提升了其独立生活能力。尽管当前辅助机器人领域的研究已分别聚焦于轮椅搭载式机械臂（WMRA）和轮椅本体，但利用机器学习模型对二者进行集成化统一控制的研究仍处于探索不足的状态。为填补这一空白，我们提出WheelArm概念——一种融合轮椅与机械臂控制的集成信息物理系统（CPS）。数据采集是开发WheelArm模型的首要步骤。本文提出WheelArm-Sim，一个基于Isaac Sim开发的用于合成数据采集的仿真框架。我们通过采集包含13项任务、232条轨迹和67,783个样本的操控-导航融合多模态数据集，评估了该框架的数据生成能力。为验证WheelArm数据集的应用潜力，我们在芥末拾取任务中构建了动作预测基线模型。实验结果表明，从WheelArm-Sim采集的数据能够有效支持数据驱动的集成控制机器学习模型开发。