UniLAP: A Unified Latent Action Policy for Cross-Embodiment Manipulation
Latent Action Models (LAMs) can capture manipulation priors from action-free videos, but discrete latents usually impose a granularity bottleneck, limiting their ability to represent fine-grained action. Recent continuous alternatives mitigate this, but they are typically used as proxies for world modeling and require a further planning step to produce executable actions. We thus introduce UniLAP, a unified continuous latent-action policy that models the latent space via flow matching and bypasses reliance on future frames during inference. By distilling a LAM teacher with a flow-adaptive objective, UniLAP inherits the teacher's action representation while retaining the multimodal distrib...
| Authors | Lei Yao, Qiao Feng, Mutian Tong et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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