DyPES-VLA: A New VLA Paradigm for Cross-Embodiment Manipulation in Heterogeneous Robots
zhenjun_zhao · x · 2026-08-07
To address the challenge of generalizing a single policy to heterogeneous robots, DyPES-VLA proposes a novel paradigm learning shared dynamics priors and embodiment-specific control.
Methodology:
- Shared Dynamics Priors: Trains the Vision-Language Model (VLM) with a future-prediction objective on cross-embodiment data, driving the shared query representation to capture object motion and scene changes.
- Embodiment-Specific Control: Utilizes a Mixture-of-Experts (MoE) action head to translate shared priors into executable controls directly in each embodiment's native action space.
- Advantages: Eliminates the need for manual pre-alignment of heterogeneous actions into a common format, while capturing common temporal action structures via shared attention layers.
Related event: DyPES-VLA: A New Paradigm for Cross-Embodiment Robot Manipulation(2 posts)→
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