Assistive teleoperation enhances efficiency via shared control, yet inter-operator variability, stemming from diverse habits and expertise, induces highly heterogeneous trajectory distributions that undermine intent recognition stability. We present Adaptor, a few-shot framework for robust cross-operator intent recognition. The Adaptor bridges the domain gap through two stages: (i) preprocessing, which models intent uncertainty by synthesizing trajectory perturbations via noise injection and performs geometry-aware keyframe extraction; and (ii) policy learning, which encodes the processed trajectories with an Intention Expert and fuses them with the pre-trained vision-language model context to condition an Action Expert for action generation. Experiments on real-world and simulated benchmarks demonstrate that Adaptor achieves state-of-the-art performance, improving success rates and efficiency over baselines. Moreover, the method exhibits low variance across operators with varying expertise, demonstrating robust cross-operator generalization.

翻译：辅助遥操作通过共享控制提升操作效率，然而源自不同习惯与专业水平的操作员间差异性，会导致高度异质的轨迹分布，从而削弱意图识别的稳定性。本文提出Adaptor——一种面向鲁棒跨操作员意图识别的小样本框架。Adaptor通过两个阶段弥合领域鸿沟：（i）预处理阶段，通过噪声注入合成轨迹扰动以建模意图不确定性，并执行几何感知的关键帧提取；（ii）策略学习阶段，利用意图专家编码处理后的轨迹，将其与预训练的视觉-语言模型上下文融合，以条件化动作专家生成动作。在真实世界与模拟基准上的实验表明，Adaptor取得了当前最优性能，相较于基线方法显著提升了成功率与效率。此外，该方法在不同专业水平的操作员间表现出低方差，展现了鲁棒的跨操作员泛化能力。