低成本机械手提升农作示范数据采集效率与精度
Advances on Affordable Hardware Platforms for Human Demonstration Acquisition in Agricultural Applications
- 从连续演示中提取任务事件，减少操作等待和认知负担
- 融合惯性测量与视觉定位，用扩展卡尔曼滤波提升轨迹可靠性
- 适用于农业场景的机器人示教，降低数据采集门槛
本文介绍了通用操作接口（UMI）在农业复杂野外环境中的进展，这是一种低成本手持夹持器，用于机器人示教学习（LfD）。重点在于以最少额外设置获取高质量示范样本。首先，通过识别任务事件从连续示范中提取独立样本，减少空闲时间和用户认知负荷；其次，结合机载惯性测量与外部视觉标记定位，利用扩展卡尔曼滤波（EKF）提升任务轨迹生成的可靠性。在果实采摘任务中，该方法性能优于默认流程。
原文摘要 · Abstract (English)
This paper presents advances on the Universal Manipulation Interface (UMI), a low-cost hand-held gripper for robot Learning from Demonstration (LfD), for complex in-the-wild scenarios found in agricultural settings. The focus is on improving the acquisition of suitable samples with minimal additional setup. Firstly, idle times and user's cognitive load are reduced through the extraction of individual samples from a continuous demonstration considering task events. Secondly, reliability on the generation of task sample's trajectories is increased through the combination on-board inertial measurements and external visual marker localization usage using Extended Kalman Filtering (EKF). Results are presented for a fruit harvesting task, outperforming the default pipeline.
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