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New framework RynnVLA-Latent scales robotic manipulation models using latent actions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RynnVLA-Latent, a new framework designed to scale the pretraining of vision-language-action (VLA) models for robotic manipulation. This approach utilizes latent actions derived from diverse video data, bypassing the need for embodiment-specific tuning and overcoming limitations of scarce action-labeled demonstrations. The RynnLAM component learns continuous, 3D-grounded latent actions from visual transitions, enabling the model to capture control-relevant motion. This framework has demonstrated high success rates across various simulated and real-world robotic benchmarks, including LIBERO, LIBERO-Plus, VLABench, and RoboTwin 2.0. AI

IMPACT This framework could significantly advance robotic manipulation capabilities by enabling more efficient and scalable training of VLA models.

RANK_REASON The cluster describes a research paper detailing a new framework and methodology for training AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework RynnVLA-Latent scales robotic manipulation models using latent actions

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The cluster describes a research paper detailing a new framework and methodology for training AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions

    RynnVLA-Latent leverages latent actions derived from diverse video to pretrain large vision-language-action policies, achieving high success across simulated and real-world robot benchmarks without embodiment-specific tuning.

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