AHEAD: Anticipatory Hand-Driven Teleoperation via Human Intent Prediction

2026-07-16

AI Summary

This work addresses the inefficiency and operator fatigue inherent in traditional hand-guided teleoperation, which relies on continuous human intervention, as well as the latency introduced by goal-based supervised methods due to command waiting periods. The authors propose AHEAD, a real-time VR teleoperation system that integrates hand and head poses with scene context over short temporal windows within a digital twin environment. AHEAD pioneers the incorporation of human intent prediction into hand-driven teleoperation by employing an attention-based classifier to forecast both grasp targets and placement locations, enabling a state machine to generate stable robot goals for proactive response. Experimental results demonstrate a top-1 intent prediction accuracy of 76%, while user studies reveal significant reductions in robot response latency—by 0.6 seconds for object selection and 1.4 seconds for slot placement—alongside markedly decreased operator workload compared to baseline approaches.

Abstract

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.

Problem

• teleoperation
• human intent prediction
• reaction latency
• operator workload
• pick-and-place tasks

Innovation

• intent prediction
• teleoperation
• anticipatory control
• attention-based classifier
• digital twin