# Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration

arXiv: https://arxiv.org/abs/1701.07790 · HTML: https://mlsanity.com/paper/1701.07790/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2017.

## Built on by (most cited first, 21 of 21)

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- [Admissible Policy Teaching through Reward Design](https://mlsanity.com/paper/2201.02185/) (2022)
- [Towards Modeling and Influencing the Dynamics of Human Learning](https://mlsanity.com/paper/2301.00901/) (2023)
- [Learning Latent Representations to Co-Adapt to Humans](https://mlsanity.com/paper/2212.09586/) (2022)
- [Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration](https://mlsanity.com/paper/2103.04077/) (2021)
- [Motivating Physical Activity via Competitive Human-Robot Interaction](https://mlsanity.com/paper/2202.07068/) (2022)
- [Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey](https://mlsanity.com/paper/2306.09445/) (2023)
- [Human adaptation to adaptive machines converges to game-theoretic equilibria](https://mlsanity.com/paper/2305.01124/) (2023)
- [Incorporating Human Flexibility through Reward Preferences in Human-AI Teaming](https://mlsanity.com/paper/2312.14292/) (2023)
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- [Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration](https://mlsanity.com/paper/2110.04347/) (2021)
- [Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs](https://mlsanity.com/paper/2310.11334/) (2023)
- [Effect of Adaptation Rate and Cost Display in a Human-AI Interaction Game](https://mlsanity.com/paper/2408.14640/) (2024)
- [Balancing Performance and Human Autonomy with Implicit Guidance Agent](https://mlsanity.com/paper/2109.00414/) (2021)
- [Uncoupled Learning of Differential Stackelberg Equilibria with Commitments](https://mlsanity.com/paper/2302.03438/) (2023)
- [Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming](https://mlsanity.com/paper/2402.05703/) (2024)
- [Open Human-Robot Collaboration using Decentralized Inverse Reinforcement Learning](https://mlsanity.com/paper/2410.01790/) (2024)
- [A Learning Algorithm That Attains the Human Optimum in a Repeated Human-Machine Interaction Game](https://mlsanity.com/paper/2501.08626/) (2025)
- [Peer-Aware Cost Estimation in Nonlinear General-Sum Dynamic Games for Mutual Learning and Intent Inference](https://mlsanity.com/paper/2504.17129/) (2025)
- [Mapping Human-Agent Co-Learning and Co-Adaptation: A Scoping Review](https://mlsanity.com/paper/2506.06324/) (2025)

## Abstract (arXiv)

In human-robot teams, humans often start with an inaccurate model of the robot capabilities. As they interact with the robot, they infer the robot's capabilities and partially adapt to the robot, i.e., they might change their actions based on the observed outcomes and the robot's actions, without replicating the robot's policy. We present a game-theoretic model of human partial adaptation to the robot, where the human responds to the robot's actions by maximizing a reward function that changes stochastically over time, capturing the evolution of their expectations of the robot's capabilities. The robot can then use this model to decide optimally between taking actions that reveal its capabilities to the human and taking the best action given the information that the human currently has. We prove that under certain observability assumptions, the optimal policy can be computed efficiently. We demonstrate through a human subject experiment that the proposed model significantly improves human-robot team performance, compared to policies that assume complete adaptation of the human to the robot.