# On Optimizing Interventions in Shared Autonomy

arXiv: https://arxiv.org/abs/2112.09169 · HTML: https://mlsanity.com/paper/2112.09169/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning](https://mlsanity.com/paper/2109.08273/) (2021)
- [Shared Autonomy via Deep Reinforcement Learning](https://mlsanity.com/paper/1802.01744/) (2018)
- [LazyDAgger: Reducing Context Switching in Interactive Imitation Learning](https://mlsanity.com/paper/2104.00053/) (2021)
- [Shared Autonomy via Hindsight Optimization](https://mlsanity.com/paper/1503.07619/) (2015)
- [Learning Human Objectives by Evaluating Hypothetical Behavior](https://mlsanity.com/paper/1912.05652/) (2019)
- [Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming](https://mlsanity.com/paper/1706.00155/) (2017)
- [Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior](https://mlsanity.com/paper/1805.08010/) (2018)
- [AvE: Assistance via Empowerment](https://mlsanity.com/paper/2006.14796/) (2020)
- [Residual Policy Learning for Shared Autonomy](https://mlsanity.com/paper/2004.05097/) (2020)
- [Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare](https://mlsanity.com/paper/2102.08507/) (2021)
- [Highly Parallelized Data-driven MPC for Minimal Intervention Shared Control](https://mlsanity.com/paper/1906.02318/) (2019)
- [Learning to Assist Drone Landings](https://mlsanity.com/paper/2011.13146/) (2020)

## Built on by (most cited first, 3 of 3)

- [To the Noise and Back: Diffusion for Shared Autonomy](https://mlsanity.com/paper/2302.12244/) (2023)
- [FlashBack: Consistency Model-Accelerated Shared Autonomy](https://mlsanity.com/paper/2505.16892/) (2025)
- [Assistron: Bayesian Shared Autonomy with Off-the-shelf Vision-Language-Action Models](https://mlsanity.com/paper/2606.23147/) (2026)

## Abstract (arXiv)

Shared autonomy refers to approaches for enabling an autonomous agent to collaborate with a human with the aim of improving human performance. However, besides improving performance, it may often also be beneficial that the agent concurrently accounts for preserving the user's experience or satisfaction of collaboration. In order to address this additional goal, we examine approaches for improving the user experience by constraining the number of interventions by the autonomous agent. We propose two model-free reinforcement learning methods that can account for both hard and soft constraints on the number of interventions. We show that not only does our method outperform the existing baseline, but also eliminates the need to manually tune a black-box hyperparameter for controlling the level of assistance. We also provide an in-depth analysis of intervention scenarios in order to further illuminate system understanding.