mlsanityData to 7 Oct 2026

On the Utility of Model Learning in HRI

arXiv 1901.01291 ↗First posted 4 Jan 2019cs.RO cs.LG stat.MLcited by 21

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Abstract (arXiv)

Fundamental to robotics is the debate between model-based and model-free learning: should the robot build an explicit model of the world, or learn a policy directly? In the context of HRI, part of the world to be modeled is the human. One option is for the robot to treat the human as a black box and learn a policy for how they act directly. But it can also model the human as an agent, and rely on a "theory of mind" to guide or bias the learning (grey box). We contribute a characterization of the performance of these methods for an autonomous driving task under the optimistic case of having an ideal theory of mind, as well as under different scenarios in which the assumptions behind the robot's theory of mind for the human are wrong, as they inevitably will be in practice.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jan 2019.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 7; 2024: 3; 2025: 0; 2026: 1.

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  1. 2021Collaborating with Humans without Human Datacited by 139
  2. 2023Large Language Models as Zero-Shot Human Models for Human-Robot Interactioncited by 30
  3. 2021Hard Choices in Artificial Intelligencecited by 26
  4. 2021Evaluating the Robustness of Collaborative Agentscited by 25
  5. 2023Adaptive Coordination in Social Embodied Rearrangementcited by 14
  6. 2021Deep Interpretable Models of Theory of Mindcited by 13
  7. 2022A Survey on Reinforcement Learning Methods in Character Animationcited by 12
  8. 2024The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Designcited by 11
  9. 2021On complementing end-to-end human behavior predictors with planningcited by 10
  10. 2022Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaborationcited by 8
  11. 2022MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communicationcited by 7
  12. 2021Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Datacited by 6
9 more
  1. 2023Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptationcited by 6
  2. 2026When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictorcited by 4
  3. 2023CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interactioncited by 3
  4. 2023Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Taskscited by 2
  5. 2023Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalizationcited by 2
  6. 2023Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learningcited by 2
  7. 2024Goal Recognition Design for General Behavioral Agents using Machine Learningcited by 2
  8. 2022Incorporating Rivalry in Reinforcement Learning for a Competitive Gamecited by 0
  9. 2024On the Utility of Accounting for Human Beliefs about AI Intention in Human-AI Collaborationcited by 0