Sparse Compositional Flow Matching by geometric assembly from motion primitives
The article discusses a new framework for matching embodied trajectories in robotics using motion primitives. This approach aims to improve the efficiency of generative models by directly composing in physical trajectory space. The framework achieves state-of-the-art accuracy, significantly reducing error ratios compared to existing methods.
- ▪The framework utilizes a compositional latent structure to enhance the efficiency of embodied AI tasks.
- ▪It incorporates a Motion-Primitive Dictionary Learning method that allows for the reuse of motion fragments.
- ▪The Structural Sparse Flow Matching with Geometric Constraints generates a binary placement matrix to ensure spatial and temporal continuity.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23341 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | dGGmxs87ryA1 |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Robotics arXiv:2605.23341 (cs) [Submitted on 22 May 2026] Title:Sparse Compositional Flow Matching by geometric assembly from motion primitives Authors:Yan Tang, Yuanbo Tang, Tingyu Cao, Shaolun Huang, Yang Li View a PDF of the paper titled Sparse Compositional Flow Matching by geometric assembly from motion primitives, by Yan Tang and 4 other authors View PDF HTML (experimental) Abstract:Embodied trajectories, such as the executable motion sequences of robotic manipulators, underwater vehicles, and mobile robots, are a fundamental output of embodied AI.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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