Adaptor: Advancing Assistive Teleoperation with Few-Shot Learning and Cross-Operator Generalization

• URL: http://arxiv.org/abs/2604.09462v1
• Date: Fri, 10 Apr 2026 16:19:43 GMT
• Title: Adaptor: Advancing Assistive Teleoperation with Few-Shot Learning and Cross-Operator Generalization
• Abstract summary: Adaptor is a framework for robust cross-operator intent recognition.<n>It bridges the domain gap through two stages: preprocessing and policy learning.<n>It achieves state-of-the-art performance, improving success rates and efficiency over baselines.
• Score: 26.90003783245582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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