Summary of Fld: Fourier Latent Dynamics For Structured Motion Representation and Learning, by Chenhao Li et al.
FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning
by Chenhao Li, Elijah Stanger-Jones, Steve Heim, Sangbae Kim
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Signal Processing (eess.SP); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method introduces a self-supervised, structured representation and generation technique that captures spatial-temporal relationships in periodic or quasi-periodic motions. This approach enhances interpolation and generalization capabilities of motion learning algorithms by mapping motion dynamics to a continuously parameterized latent space. The resulting motion learning controller can track a wide range of motions, including unseen targets during training, while incorporating a fallback mechanism for safe action execution when necessary. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to understand and learn about motion patterns. It creates a special system that can recognize and generate different types of movements by finding hidden connections between the timing and location of these movements. This helps machines learn more accurately and make decisions even when they’ve never seen certain motions before. The system also includes a safety feature that prevents it from taking risky actions if it’s unsure what to do. |
Keywords
* Artificial intelligence * Generalization * Latent space * Self supervised
