# Human-Robot Mutual Adaptation in Shared Autonomy

arXiv: https://arxiv.org/abs/1701.07851 · HTML: https://mlsanity.com/paper/1701.07851/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2017.

## Built on by (most cited first, 16 of 16)

- [Physical Interaction as Communication: Learning Robot Objectives Online from Human Corrections](https://mlsanity.com/paper/2107.02349/) (2021)
- [PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection](https://mlsanity.com/paper/2212.04708/) (2022)
- [X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback](https://mlsanity.com/paper/2203.02072/) (2022)
- [Utility Theory based Cognitive Modeling in the Application of Robotics: A Survey](https://mlsanity.com/paper/2306.09445/) (2023)
- [End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning](https://mlsanity.com/paper/2104.12842/) (2021)
- [Towards Cooperative Flight Control Using Visual-Attention](https://mlsanity.com/paper/2212.11084/) (2022)
- [A behavioural transformer for effective collaboration between a robot and a non-stationary human](https://mlsanity.com/paper/2307.13447/) (2023)
- [Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation](https://mlsanity.com/paper/2111.06742/) (2021)
- [Robust Robot Planning for Human-Robot Collaboration](https://mlsanity.com/paper/2302.13916/) (2023)
- [Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models](https://mlsanity.com/paper/2506.14727/) (2025)
- [Robots Influencing Humans to Reveal their Goals during Collaboration and Competition](https://mlsanity.com/paper/2609.05519/) (2026)
- [Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming](https://mlsanity.com/paper/2402.05703/) (2024)
- [Combating Spatial Disorientation in a Dynamic Self-Stabilization Task Using AI Assistants](https://mlsanity.com/paper/2409.14565/) (2024)
- [Vision-Based Mistake Analysis in Procedural Activities: A Review of Advances and Challenges](https://mlsanity.com/paper/2510.19292/) (2025)
- [End-to-end Optimization of Belief and Policy Learning in Shared Autonomy Paradigms](https://mlsanity.com/paper/2601.23285/) (2026)
- [Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG](https://mlsanity.com/paper/2603.09011/) (2026)

## Abstract (arXiv)

Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own preference, while still retaining his trust. We achieve this through a principled human-robot mutual adaptation formalism. We integrate a bounded-memory adaptation model of the human into a partially observable stochastic decision model, which enables the robot to adapt to an adaptable human. When the human is adaptable, the robot guides the human towards a good strategy, maybe unknown to the human in advance. When the human is stubborn and not adaptable, the robot complies with the human's preference in order to retain their trust. In the shared autonomy setting, unlike many other common human-robot collaboration settings, only the robot actions can change the physical state of the world, and the human and robot goals are not fully observable. We address these challenges and show in a human subject experiment that the proposed mutual adaptation formalism improves human-robot team performance, while retaining a high level of user trust in the robot, compared to the common approach of having the robot strictly following participants' preference.