mlsanityData to 14 Oct 2026

Optimal Interactive Learning on the Job via Facility Location Planning

arXiv 2505.00490 ↗First posted 1 May 2025cs.RO cs.AIcited by 2

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Abstract (arXiv)

Collaborative robots must continually adapt to novel tasks and user preferences without overburdening the user. While prior interactive robot learning methods aim to reduce human effort, they are typically limited to single-task scenarios and are not well-suited for sustained, multi-task collaboration. We propose COIL (Cost-Optimal Interactive Learning) -- a multi-task interaction planner that minimizes human effort across a sequence of tasks by strategically selecting among three query types (skill, preference, and help). When user preferences are known, we formulate COIL as an uncapacitated facility location (UFL) problem, which enables bounded-suboptimal planning in polynomial time using off-the-shelf approximation algorithms. We extend our formulation to handle uncertainty in user preferences by incorporating one-step belief space planning, which uses these approximation algorithms as subroutines to maintain polynomial-time performance. Simulated and physical experiments on manipulation tasks show that our framework significantly reduces the amount of work allocated to the human while maintaining successful task completion.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 1 May 2025.

Builds on

21 of 37 references.

References · 21

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  2. 2017Deep reinforcement learning from human preferencescited by 3,912
  3. 2020robosuite: A Modular Simulation Framework and Benchmark for Robot Learningcited by 511
  4. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 327
  5. 2016Cooperative Inverse Reinforcement Learningcited by 300
  6. 2020Reward-rational (implicit) choice: A unifying formalism for reward learningcited by 120
  7. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 108
  8. 2023Contrastive Preference Learning: Learning from Human Feedback without RLcited by 79
  9. 2020Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferencescited by 71
  10. 2013Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizationscited by 70
  11. 2023Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferencescited by 26
  12. 2022Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human-Robot Interactioncited by 19
9 more
  1. 2024Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedbackcited by 11
  2. 2024Learning Efficient and Robust Language-conditioned Manipulation using Textual-Visual Relevancy and Equivariant Language Mappingcited by 10
  3. 2024Batch Active Learning of Reward Functions from Human Preferencescited by 8
  4. 2024Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Inputcited by 8
  5. 2024Learning Human Preferences Over Robot Behavior as Soft Planning Constraintscited by 6
  6. 2022Synergistic Scheduling of Learning and Allocation of Tasks in Human-Robot Teamscited by 5
  7. 2024Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Frameworkcited by 5
  8. 2020Learning to Optimize Autonomy in Competence-Aware Systemscited by 4
  9. 2024PECAN: Personalizing Robot Behaviors through a Learned Canonical Spacecited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teamingcited by 0
  2. 2026Deliberate Practice: Learning Robot Skills under a Budgetcited by 0