mlsanityData to 14 Oct 2026

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

arXiv 2505.15503 ↗First posted 21 May 2025cs.RO cs.LGcited by 1

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Abstract (arXiv)

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/

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 30 of its 61 references that are in the map. First posted on arXiv 21 May 2025.

Builds on

30 of 61 references.

References · 30

  1. 2024ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulationcited by 315
  2. 2020Integrated Task and Motion Planningcited by 290
  3. 2023Text2Motion: From Natural Language Instructions to Feasible Planscited by 224
  4. 2023TidyBot: Personalized Robot Assistance with Large Language Modelscited by 221
  5. 2018PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planningcited by 150
  6. 2023Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Modelscited by 91
  7. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  8. 2025Lifelong Learning of Large Language Model based Agents: A Roadmapcited by 83
  9. 2022Predicate Invention for Bilevel Planningcited by 49
  10. 2020Learning compositional models of robot skills for task and motion planningcited by 48
  11. 2024A Survey of Constraint Formulations in Safe Reinforcement Learningcited by 44
  12. 2024VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planningcited by 39
18 more
  1. 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyondcited by 25
  2. 2024Practice Makes Perfect: Planning to Learn Skill Parameter Policiescited by 23
  3. 2024Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfactioncited by 19
  4. 2023Embodied Lifelong Learning for Task and Motion Planningcited by 17
  5. 2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applicationscited by 15
  6. 2022Learning Representations that Enable Generalization in Assistive Taskscited by 13
  7. 2024APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMscited by 11
  8. 2024From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Modelscited by 11
  9. 2024FLAIR: Feeding via Long-horizon AcquIsition of Realistic dishescited by 10
  10. 2021Balancing Efficiency and Comfort in Robot-Assisted Bite Transfercited by 8
  11. 2023Embodied Active Learning of Relational State Abstractions for Bilevel Planningcited by 8
  12. 2025Bilevel Learning for Bilevel Planningcited by 7
  13. 2022SPARCS: Structuring Physically Assistive Robotics for Caregiving with Stakeholders-in-the-loopcited by 5
  14. 2024Text2Interaction: Establishing Safe and Preferable Human-Robot Interactioncited by 4
  15. 2024Personalization in Human-Robot Interaction through Preference-based Action Representation Learningcited by 4
  16. 2025GRACE: Generalizing Robot-Assisted Caregiving with User Functionality Embeddingscited by 4
  17. 2024Robot Behavior Personalization from Sparse User Feedbackcited by 2
  18. 2023A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehiclescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraintscited by 7