QuickLAP combines physical corrections with LLM-parsed natural language in a closed-form Bayesian update, reducing reward-learning error in simulated driving and improving user ratings in a 15-person study.
Conformalized teleoperation: Confidently mapping human inputs to high-dimensional robot actions
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
Adaptor uses few-shot learning with trajectory perturbation and vision-language conditioning to achieve robust cross-operator intent recognition and higher success rates in assistive teleoperation.
citing papers explorer
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QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents
QuickLAP combines physical corrections with LLM-parsed natural language in a closed-form Bayesian update, reducing reward-learning error in simulated driving and improving user ratings in a 15-person study.
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Adaptor: Advancing Assistive Teleoperation with Few-Shot Learning and Cross-Operator Generalization
Adaptor uses few-shot learning with trajectory perturbation and vision-language conditioning to achieve robust cross-operator intent recognition and higher success rates in assistive teleoperation.