Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation
Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process. The resulting velocity field must simultaneously encode scene information and generate precise trajectories, increasing learning complexity and limiting performance on tasks demanding precise temporal coordination across multiple arms. To simplify this joint learning problem, we introduce Latent Diffusion Policy (LDP), a two-stage framework performing flow matching in a deliberately shaped latent space. By absorbing scene understanding into an observation-conditioned CVAE encoder, LDP concentrates the conditional distributio...
| Authors | Zhexuan Zhou, Yichen Lai, Jinhao Zhang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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