TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization
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| Format: | Preprint |
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2025
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| _version_ | 1866914060475301888 |
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| author | Ding, Yuxuan Wang, Shuangge Fitzgerald, Tesca |
| author_facet | Ding, Yuxuan Wang, Shuangge Fitzgerald, Tesca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization Ding, Yuxuan Wang, Shuangge Fitzgerald, Tesca Robotics Artificial Intelligence 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. |
| title | TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.00310 |