AHEAD：通过人类意图预测实现预期的手动驱动遥操作
AHEAD: Anticipatory Hand-Driven Teleoperation via Human Intent Prediction
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中文总结 AI 辅助
研究旨在减少机器人反应时间并降低操作员工作量，提出AHEAD实时VR遥操作系统，通过处理手和头部信号及场景上下文预测意图，转换为稳定目标，该系统意图预测准确率高，能有效减少延迟并降低操作员负荷。
中文摘要 AI 辅助
直接手动驱动遥操作能精确控制，但在接近、抓取和放置过程中需持续监控和校正，效率低且易疲劳。监督式遥操作虽简化流程，但有延迟。为解决如何减少机器人反应时间并降低操作员工作量的问题，提出AHEAD实时VR遥操作系统。在数字孪生中，操作员自然执行抓取和放置，AHEAD通过基于注意力的分类器处理手和头部信号及场景上下文来预测意图，状态机将意图预测转换为稳定目标。其意图预测模块在抓取对象和目标插槽上的Top1准确率达76%，用户研究表明AHEAD相对于基线分别减少了0.6秒（对象）和1.4秒（插槽）的机器人反应延迟，还降低了操作员负荷。
英文摘要
Direct hand-driven teleoperation maps an operator's hand motion to robot end-effector commands at every frame, enabling precise control, but it requires constant monitoring and correction during approach, grasp, and placement, which can be slow and fatiguing. For repetitive pick-and-place tasks, supervisory (goal-based) teleoperation simplifies this process: the operator specifies goals/waypoints, and the robot executes the motion using planning algorithms. Yet, this introduces latency, as the robot must wait for the next command before it can plan and act. "How can we reduce robot reaction time while lowering operator workload?" To tackle this question, we present AHEAD, a real-time VR teleoperation system that anticipates operator intent to enable proactive, hand-driven control. In a digital twin, the operator performs pick-and-place naturally, using hand motion to convey high-level commands rather than a continuous robot trajectory. AHEAD processes a short window of 3D hand and head signals together with scene context through an attention-based classifier to predict the intended grasp object and placement slot. A state machine converts intent predictions into stable robot goals, enabling early motion while remaining stable under noisy predictions and corrective hand movements. AHEAD's intent prediction module achieves Top1 accuracy: 76% for grasp objects and 76% for target slots. Moreover, our user study shows AHEAD reduces robot reaction latency by 0.6 s (object) and 1.4 s (slot) relative to baselines. Participants also reported lower operator load, indicating faster robot responses while maintaining low operator effort in practice.
发表机构
- Georgia Institute of Technology（佐治亚理工学院）
- Neuromeka Ltd.（Neuromeka有限公司）
- INRIA（法国国家信息与自动化研究所）
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
