Sparse Compositional Flow Matching by geometric assembly from motion primitives
Embodied trajectories, such as the executable motion sequences of robotic manipulators, underwater vehicles, and mobile robots, are a fundamental output of embodied AI. Modern generative models often treat them as a dense, monolithic signal generated point by point, fitting an intricate high-dimensional posterior while leaving the data's latent structure unmodeled, the same sample inefficiency long identified by the structured generative model literature. We argue that a compositional latent structure is a natural choice: many embodied tasks share recurring motion fragments that can be made explicit as a finite repertoire of reusable motion primitives, and compositional units naturally al...
| Authors | Yan Tang, Yuanbo Tang, Tingyu Cao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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