Adaptor: 少数ショット学習とオペレータ横断汎化による支援遠隔操作の高度化
Adaptor: Advancing Assistive Teleoperation with Few-Shot Learning and Cross-Operator Generalization
支援遠隔操作におけるオペレータ間の意図認識のばらつきを、ノイズ注入による軌道摂動と幾何学的キーフレーム抽出、および視覚言語モデルとの融合を用いた少数ショット学習フレームワークで解決する。
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著者: Yu Liu, Yihang Yin, Tianlv Huang, Fei Yan, Yuan Xu, Weinan Hong, Wei Han, Yue Cao, Xiangyu Chen, Zipei Fan, Xuan Song
分類: cs.RO
原文アブストラクト
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.
