# Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation

arXiv: https://arxiv.org/abs/2403.16178 · HTML: https://mlsanity.com/paper/2403.16178/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- ["Other-Play" for Zero-Shot Coordination](https://mlsanity.com/paper/2003.02979/) (2020)
- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Uncertainty-Aware Reinforcement Learning for Collision Avoidance](https://mlsanity.com/paper/1702.01182/) (2017)
- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination](https://mlsanity.com/paper/2112.11701/) (2021)
- [When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans](https://mlsanity.com/paper/2001.04377/) (2020)
- [Shared Autonomy with Learned Latent Actions](https://mlsanity.com/paper/2005.03210/) (2020)
- [The Utility of Explainable AI in Ad Hoc Human-Machine Teaming](https://mlsanity.com/paper/2209.03943/) (2022)
- [PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination](https://mlsanity.com/paper/2301.06387/) (2023)
- [Learning in POMDPs with Monte Carlo Tree Search](https://mlsanity.com/paper/1806.05631/) (2018)
- [Mixed-Initiative variable autonomy for remotely operated mobile robots](https://mlsanity.com/paper/1911.04848/) (2019)
- [Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration](https://mlsanity.com/paper/2008.00699/) (2020)

## Built on by (most cited first, 2 of 2)

- [Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation](https://mlsanity.com/paper/2508.05535/) (2025)
- [YRC-Bench: A Benchmark for Learning to Coordinate with Experts](https://mlsanity.com/paper/2502.09583/) (2025)

## Abstract (arXiv)

For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accordingly. Most prior works make the unrealistic assumption that one or more teammates can act near-optimally. In real-world collaboration, humans and autonomous agents can be suboptimal, especially when each only has partial domain knowledge. In this work, we develop computational modeling and optimization techniques for enhancing the performance of suboptimal human-agent teams, where the human and the agent have asymmetric capabilities and act suboptimally due to incomplete environmental knowledge. We adopt an online Bayesian approach that enables a robot to infer people's willingness to comply with its assistance in a sequential decision-making game. Our user studies show that user preferences and team performance indeed vary with robot intervention styles, and our approach for mixed-initiative collaborations enhances objective team performance (p<.001) and subjective measures, such as user's trust (p<.001) and willingness to comply with the robot (p<.001).