Coloring Between the Lines: Personalization in the Null Space of Planning Constraints

TL;DR

This paper introduces CBTL, a novel method enabling robots to personalize behavior safely and efficiently by exploiting the null space of planning constraints, demonstrated through simulations, user studies, and real-robot experiments.

Contribution

CBTL is a new approach that leverages the null space of CSPs for safe, sample-efficient robot personalization without environment resets.

Findings

CBTL outperforms baselines in diverse environments.

CBTL achieves effective personalization with fewer interactions.

CBTL demonstrates success in real-robot assisted feeding.

Abstract

Generalist robots must personalize in-the-wild to meet the diverse needs and preferences of long-term users. How can we enable flexible personalization without sacrificing safety or competency? This paper proposes Coloring Between the Lines (CBTL), a method for personalization that exploits the null space of constraint satisfaction problems (CSPs) used in robot planning. CBTL begins with a CSP generator that ensures safe and competent behavior, then incrementally personalizes behavior by learning parameterized constraints from online interaction. By quantifying uncertainty and leveraging the compositionality of planning constraints, CBTL achieves sample-efficient adaptation without environment resets. We evaluate CBTL in (1) three diverse simulation environments; (2) a web-based user study; and (3) a real-robot assisted feeding system, finding that CBTL consistently achieves more effective personalization with fewer interactions than baselines. Our results demonstrate that CBTL provides a unified and practical approach for continual, flexible, active, and safe robot personalization. Website: https://emprise.cs.cornell.edu/cbtl/