# Scaled Autonomy: Enabling Human Operators to Control Robot Fleets

arXiv: https://arxiv.org/abs/1910.02910 · HTML: https://mlsanity.com/paper/1910.02910/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2019.

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

- [A Minimaximalist Approach to Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2401.04056/) (2024)
- [ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning](https://mlsanity.com/paper/2109.08273/) (2021)
- [Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap](https://mlsanity.com/paper/2103.03236/) (2021)
- [Cooperative Exploration for Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2107.11444/) (2021)
- [LazyDAgger: Reducing Context Switching in Interactive Imitation Learning](https://mlsanity.com/paper/2104.00053/) (2021)
- [Multi-Task Interactive Robot Fleet Learning with Visual World Models](https://mlsanity.com/paper/2410.22689/) (2024)
- [Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision](https://mlsanity.com/paper/2206.14349/) (2022)
- [Context-aware Communication for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2312.15600/) (2023)
- [ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward](https://mlsanity.com/paper/2210.04365/) (2022)
- [Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2502.19717/) (2025)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [Grounded Answers for Multi-agent Decision-making Problem through Generative World Model](https://mlsanity.com/paper/2410.02664/) (2024)
- [Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2410.08540/) (2024)
- [Cooperation for Scalable Supervision of Autonomy in Mixed Traffic](https://mlsanity.com/paper/2112.07569/) (2021)
- [Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2405.18110/) (2024)
- [Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments](https://mlsanity.com/paper/2506.07232/) (2025)

## Abstract (arXiv)

Autonomous robots often encounter challenging situations where their control policies fail and an expert human operator must briefly intervene, e.g., through teleoperation. In settings where multiple robots act in separate environments, a single human operator can manage a fleet of robots by identifying and teleoperating one robot at any given time. The key challenge is that users have limited attention: as the number of robots increases, users lose the ability to decide which robot requires teleoperation the most. Our goal is to automate this decision, thereby enabling users to supervise more robots than their attention would normally allow for. Our insight is that we can model the user's choice of which robot to control as an approximately optimal decision that maximizes the user's utility function. We learn a model of the user's preferences from observations of the user's choices in easy settings with a few robots, and use it in challenging settings with more robots to automatically identify which robot the user would most likely choose to control, if they were able to evaluate the states of all robots at all times. We run simulation experiments and a user study with twelve participants that show our method can be used to assist users in performing a simulated navigation task. We also run a hardware demonstration that illustrates how our method can be applied to a real-world mobile robot navigation task.