Motion Manifold Flow Primitives for Language-Guided Trajectory Generation
<|reference_start|>Motion Manifold Flow Primitives for Language-Guided Trajectory Generation: Developing text-based robot trajectory generation models is made particularly difficult by the small dataset size, high dimensionality of the trajectory space, and the inherent complexity of the text-conditional motion distribution. Recent manifold learning-based methods have partially addressed the dimensionality and dataset size issues, but struggle with the complex text-conditional distribution. In this paper we propose a text-based trajectory generation model that attempts to address all three challenges while relying on only a handful of demonstration trajectory data. Our key idea is to leverage recent flow-based models capable of capturing complex conditional distributions, not directly in the high-dimensional trajectory space, but rather in the low-dimensional latent coordinate space of the motion manifold, with deliberately designed regularization terms to ensure smoothness of motions and robustness to text variations. We show that our {\it Motion Manifold Flow Primitive (MMFP)} framework can accurately generate qualitatively distinct motions for a wide range of text inputs, significantly outperforming existing methods.<|reference_end|>
@article{lee2024motion,
    title={Motion Manifold Flow Primitives for Language-Guided Trajectory
  Generation},
    author={Yonghyeon Lee, Byeongho Lee, Seungyeon Kim, Frank C. Park},
    journal={arXiv preprint arXiv:2407.19681},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19681},
    primaryClass={cs.RO cs.AI}
}