Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model
Dynin-Robotics omnimodal diffusion vision-language-action model unifies action, dynamics, and goal prediction, reaching 78.4% success on Franka tasks.
Dynin-Robotics builds a shared trajectory model on the Dynin-Omni omnimodal masked-diffusion backbone, representing language, observations, goals, and actions as discrete tokens. One model learns action prediction, action-conditioned next-observation prediction, terminal goal-state prediction, and trajectory-to-instruction reconstruction, enabling test-time scaling through goal prediction and joint refinement. It is continually pretrained on approximately 1.33 million trajectories from 48 Open X-Embodiment datasets and achieves competitive performance on LIBERO and zero-shot LIBERO-Plus plus a 78.4% average success rate across four manipulation conditions on a Franka Research 3 robot. An optimized block-parallel implementation accelerates model-side action decoding by up to 29.2x.
- Single diffusion model learns action, dynamics, goal, and instruction reconstruction objectives.
- Pretrained on ~1.33M trajectories from 48 Open X-Embodiment datasets.
- 78.4% average success across four conditions on Franka Research 3.
- Competitive on LIBERO and zero-shot LIBERO-Plus.
- Block-parallel decoding gives up to 29.2x speedup.
From the original article
1 min read at arxiv.orgVisual goal and dynamics prediction can provide language-conditioned robot policies with both a target outcome and a representation of action-dependent scene changes. We bring these predictions into action generation and selection through a shared trajectory model.
By Hoeun Lee · arxiv.org
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