BlockVLA accelerates autoregressive VLA models by 3.3x using block diffusion finetuning, with faster training convergence and better early performance on long-horizon robotic tasks.
Embodied robot manipulation in the era of foundation models: Planning and learning perspectives
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A co-evolutionary VLM-VGM loop on 500 unlabeled images raises planner success by 30 points and simulator success by 48 percent while beating fully supervised baselines.
HEX introduces a state-centric framework with humanoid-aligned representations and mixture-of-experts proprioceptive prediction for coordinated whole-body control on bipedal humanoids.
citing papers explorer
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BlockVLA: Accelerating Autoregressive VLA via Block Diffusion Finetuning
BlockVLA accelerates autoregressive VLA models by 3.3x using block diffusion finetuning, with faster training convergence and better early performance on long-horizon robotic tasks.
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RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data
A co-evolutionary VLM-VGM loop on 500 unlabeled images raises planner success by 30 points and simulator success by 48 percent while beating fully supervised baselines.
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HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation
HEX introduces a state-centric framework with humanoid-aligned representations and mixture-of-experts proprioceptive prediction for coordinated whole-body control on bipedal humanoids.