LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation
We introduce LIBERO-PeRM, a benchmark for preference learning in embodied agents.
ProofPaper ↗
Key points
- 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.
- 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; and 5) how policies compose preferences and behave under conflicts.
- We propose a controlled formulation and taxonomy of observable preferences in simulation, covering motion, skill, and user-profile choices.
Sources (1)
- [1]LIBERO-PeRM: Benchmarking Personalized Robotic ManipulationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:59 PM
We introduce LIBERO-PeRM, a benchmark for preference learning in embodied agents.
Personalization is essential for general-purpose robots to transition into household environments and achieve true practicality.
Extractive summary: sentences quoted from the sources.
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