# A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy

arXiv: https://arxiv.org/abs/2012.04283 · HTML: https://mlsanity.com/paper/2012.04283/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 20 of 20)

- [Differentiable Quality Diversity](https://mlsanity.com/paper/2106.03894/) (2021)
- [Deep Surrogate Assisted Generation of Environments](https://mlsanity.com/paper/2206.04199/) (2022)
- [Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning](https://mlsanity.com/paper/2202.03666/) (2022)
- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [pyribs: A Bare-Bones Python Library for Quality Diversity Optimization](https://mlsanity.com/paper/2303.00191/) (2023)
- [Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2205.10752/) (2022)
- [PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection](https://mlsanity.com/paper/2212.04708/) (2022)
- [Multi-Robot Coordination and Layout Design for Automated Warehousing](https://mlsanity.com/paper/2305.06436/) (2023)
- [CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories](https://mlsanity.com/paper/2403.13208/) (2024)
- [Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2210.02622/) (2022)
- [The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers](https://mlsanity.com/paper/2303.16207/) (2023)
- [Arbitrarily Scalable Environment Generators via Neural Cellular Automata](https://mlsanity.com/paper/2310.18622/) (2023)
- [Surrogate Assisted Generation of Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2304.13787/) (2023)
- [Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity](https://mlsanity.com/paper/2210.09918/) (2022)
- [Hierarchical Quality-Diversity for Online Damage Recovery](https://mlsanity.com/paper/2204.05726/) (2022)
- [Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks](https://mlsanity.com/paper/2211.06134/) (2022)
- [Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity](https://mlsanity.com/paper/2411.04466/) (2024)
- [Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies](https://mlsanity.com/paper/2603.12510/) (2026)
- [Algorithmic Scenario Generation as Quality Diversity Optimization](https://mlsanity.com/paper/2409.04711/) (2024)
- [Towards Interpretable Foundation Models of Robot Behavior: A Task Specific Policy Generation Approach](https://mlsanity.com/paper/2407.08065/) (2024)

## Abstract (arXiv)

The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring diverse scenarios of humans and robots interacting in simulation can improve understanding of the robotic system and avoid potentially costly failures in real-world settings. We formulate this problem as a quality diversity (QD) problem, where the goal is to discover diverse failure scenarios by simultaneously exploring both environments and human actions. We focus on the shared autonomy domain, where the robot attempts to infer the goal of a human operator, and adopt the QD algorithm MAP-Elites to generate scenarios for two published algorithms in this domain: shared autonomy via hindsight optimization and linear policy blending. Some of the generated scenarios confirm previous theoretical findings, while others are surprising and bring about a new understanding of state-of-the-art implementations. Our experiments show that MAP-Elites outperforms Monte-Carlo simulation and optimization based methods in effectively searching the scenario space, highlighting its promise for automatic evaluation of algorithms in human-robot interaction.