LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation
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.
Zhi-Xu Li, Ke Tang, Li-Tian Gong et al.
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