{"ID":2876378,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00310","arxiv_id":"2509.00310","title":"TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization","abstract":"Robots often struggle to generalize from a single demonstration due to the lack of a transferable and interpretable spatial representation. In this work, we introduce TReF-6, a method that infers a simplified, abstracted 6DoF Task-Relevant Frame from a single trajectory. Our approach identifies an influence point purely from the trajectory geometry to define the origin for a local frame, which serves as a reference for parameterizing a Dynamic Movement Primitive (DMP). This influence point captures the task's spatial structure, extending the standard DMP formulation beyond start-goal imitation. The inferred frame is semantically grounded via a vision-language model and localized in novel scenes by Grounded-SAM, enabling functionally consistent skill generalization. We validate TReF-6 in simulation and demonstrate robustness to trajectory noise. We further deploy an end-to-end pipeline on real-world manipulation tasks, showing that TReF-6 supports one-shot imitation learning that preserves task intent across diverse object configurations.","short_abstract":"Robots often struggle to generalize from a single demonstration due to the lack of a transferable and interpretable spatial representation. In this work, we introduce TReF-6, a method that infers a simplified, abstracted 6DoF Task-Relevant Frame from a single trajectory. Our approach identifies an influence point purel...","url_abs":"https://arxiv.org/abs/2509.00310","url_pdf":"https://arxiv.org/pdf/2509.00310v2","authors":"[\"Yuxuan Ding\",\"Shuangge Wang\",\"Tesca Fitzgerald\"]","published":"2025-08-30T01:54:28Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}