LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation
Personalization is essential for general-purpose robots to transition into household environments and achieve true practicality. While general robotic manipulation has been extensively studied, personalized robotic manipulation (PeRM) lacks a controlled benchmark for separating task completion from preference satisfaction. We introduce LIBERO-PeRM, a benchmark for preference learning in embodied agents. LIBERO-PeRM studies five questions: 1) how policies follow preferences in zero-shot settings; 2) how they learn single preferences from demonstrations; 3) how efficiently they acquire preferences as demonstrations increase; 4) how learned preferences generalize across layouts and tasks; an...
| Authors | Zhixu Li, Keqian Tang, Litian Gong et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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