MotuBrain jointly models video and action via a three-stream Mixture-of-Transformers UniDiffuser to reach 95.8-96.1% success on RoboTwin 2.0 benchmarks, top EWMScore, and fast 11 Hz inference while adapting to new robots with 50-100 trajectories.
Openvla: An open-source vision-language-action model
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2026 4representative citing papers
UAV-Track VLA modifies the π0.5 VLA architecture with temporal compression and dual-branch decoding to reach 61.76% success and 269.65 average frames in long-distance pedestrian tracking on a new 890K-frame UAV dataset, while cutting inference latency by 33.4%.
DIAL decouples intent from action in end-to-end VLAs using a latent visual foresight bottleneck and two-stage training, reaching SOTA on RoboCasa with 10x fewer demonstrations and zero-shot real-world transfer.
Generative VLAs hallucinate physically invalid actions due to topological, precision, and horizon mismatches between model architectures and feasible robot behavior.
citing papers explorer
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MotuBrain: An Advanced World Action Model for Robot Control
MotuBrain jointly models video and action via a three-stream Mixture-of-Transformers UniDiffuser to reach 95.8-96.1% success on RoboTwin 2.0 benchmarks, top EWMScore, and fast 11 Hz inference while adapting to new robots with 50-100 trajectories.
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UAV-Track VLA: Embodied Aerial Tracking via Vision-Language-Action Models
UAV-Track VLA modifies the π0.5 VLA architecture with temporal compression and dual-branch decoding to reach 61.76% success and 269.65 average frames in long-distance pedestrian tracking on a new 890K-frame UAV dataset, while cutting inference latency by 33.4%.
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DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA
DIAL decouples intent from action in end-to-end VLAs using a latent visual foresight bottleneck and two-stage training, reaching SOTA on RoboCasa with 10x fewer demonstrations and zero-shot real-world transfer.
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Action Hallucination in Generative Vision-Language-Action Models
Generative VLAs hallucinate physically invalid actions due to topological, precision, and horizon mismatches between model architectures and feasible robot behavior.