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Experiments show that current VLA policies struggle with zero-shot personalization, while SFT makes preferences learnable, and a controlled formulation and taxonomy of observable preferences in simulation is proposed, covering motion, skill, and user-profile choices.
RoboFolDeX is introduced, a physical-world benchmark built entirely from real-robot data, with garment folding as its primary task, and a fair real-robot evaluation platform for externally submitted policies, with standardized tasks, held-out physical objects, controlled initializations, and a unified execution protoco...
Experiments on simulated and real-robot manipulation benchmarks demonstrate that EDAR improves downstream policy learning, especially in long-horizon manipulation, highlighting the importance of grounding action representations in executable control structure and environment-conditioned visual change.
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