无需预设目标，实时推断人类操作意图，提升机器人协作效率。
GUIDER: Evaluating Goal-Free Human Intent Inference for Teleoperated Manipulation on Real-Robot Data
- 基于概率框架，不依赖预设目标，实时推断人机协作意图。
- 在3种真实场景中，20次操作全部正确识别可抓取区域，预测稳定率达96.4%。
- 适合需要自然交互的机器人辅助任务，如医疗协助或家庭服务。
本文评估了一种无目标设定的随机性人类意图推断框架在实际机器人操作中的表现。我们将全局用户意图双阶段估计机器人（GUIDER）部署于机械臂采集的真实数据上，测试其在泡茶、取药等多种协助场景下的操作阶段表现。为支持实际运行，引入在线概率更新、工作空间限制、支撑面滤波及优先可行抓取区的抓取模式，并在保留原始时间条件的前提下验证其效果。在三个场景共20个操作步骤中，GUIDER在所有情况下均将人类意图准确估计在正确抓取候选集合内，实现3.7秒的置信预测耗时、首次抓取前49.6秒的剩余时间预测、96.4%的预测稳定性以及每次感知阶段4.857/4.474秒（均值/中位数）的运行时间。
原文摘要 · Abstract (English)
This paper presents an evaluation of a goal-free probabilistic framework for human intent inference during robotic manipulation. We deploy the Global User Intent Dual-phase Estimation for Robots (GUIDER) on data collected from a robotic arm to test the manipulation phase across various assistance scenarios, including making tea and fetching medicine. To support operation, we add online probability updates, workspace limits, support-plane filtering, and a grasping mode that prioritizes feasible grasp regions, all of which are tested on the recorded data while preserving its original temporal conditions. Across 20 manipulation steps in three scenarios, GUIDER estimated human intent within the correct grasp-candidate set in all cases and achieved a time to confident prediction of 3.7 s, a remaining time before first grasp of 49.6 s, a prediction stability of 96.4%, and a runtime of 4.857/4.474 s (mean/median) per perceptual phase of intent.
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