# Shared Autonomy via Hindsight Optimization

arXiv: https://arxiv.org/abs/1503.07619 · HTML: https://mlsanity.com/paper/1503.07619/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2015.

## Built on by (most cited first, 30 of 46)

- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment](https://mlsanity.com/paper/2211.08416/) (2022)
- [Active Preference-Based Gaussian Process Regression for Reward Learning](https://mlsanity.com/paper/2005.02575/) (2020)
- [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://mlsanity.com/paper/2405.10315/) (2024)
- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models](https://mlsanity.com/paper/2204.10759/) (2022)
- [On the Sensitivity of Reward Inference to Misspecified Human Models](https://mlsanity.com/paper/2212.04717/) (2022)
- [To the Noise and Back: Diffusion for Shared Autonomy](https://mlsanity.com/paper/2302.12244/) (2023)
- [PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection](https://mlsanity.com/paper/2212.04708/) (2022)
- [Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects](https://mlsanity.com/paper/2104.12149/) (2021)
- [Situational Confidence Assistance for Lifelong Shared Autonomy](https://mlsanity.com/paper/2104.06556/) (2021)
- [Conditional Imitation Learning for Multi-Agent Games](https://mlsanity.com/paper/2201.01448/) (2022)
- [Active Reward Learning from Online Preferences](https://mlsanity.com/paper/2302.13507/) (2023)
- [Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings](https://mlsanity.com/paper/2206.12081/) (2022)
- [Intention estimation from gaze and motion features for human-robot shared-control object manipulation](https://mlsanity.com/paper/2208.08688/) (2022)
- [Gaze-based intention estimation: principles, methodologies, and applications in HRI](https://mlsanity.com/paper/2302.04530/) (2023)
- [Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition](https://mlsanity.com/paper/2407.00299/) (2024)
- [MILE: Model-based Intervention Learning](https://mlsanity.com/paper/2502.13519/) (2025)
- [AssistanceZero: Scalably Solving Assistance Games](https://mlsanity.com/paper/2504.07091/) (2025)
- [Analyzing Human Models that Adapt Online](https://mlsanity.com/paper/2103.05746/) (2021)
- [Learning Altruistic Behaviours in Reinforcement Learning without External Rewards](https://mlsanity.com/paper/2107.09598/) (2021)
- [ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2202.02465/) (2022)
- [Surrogate Assisted Generation of Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2304.13787/) (2023)
- [Toward AI Assistants That Let Designers Design](https://mlsanity.com/paper/2107.13074/) (2021)
- [Zero-Shot Assistance in Sequential Decision Problems](https://mlsanity.com/paper/2202.07364/) (2022)
- [On Optimizing Interventions in Shared Autonomy](https://mlsanity.com/paper/2112.09169/) (2021)
- [Shared Autonomy with IDA: Interventional Diffusion Assistance](https://mlsanity.com/paper/2409.15317/) (2024)
- [Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement](https://mlsanity.com/paper/2606.18247/) (2026)
- [Collaborative Decision Making Using Action Suggestions](https://mlsanity.com/paper/2209.13160/) (2022)
- [Towards Cooperative Flight Control Using Visual-Attention](https://mlsanity.com/paper/2212.11084/) (2022)

## Abstract (arXiv)

In shared autonomy, user input and robot autonomy are combined to control a robot to achieve a goal. Often, the robot does not know a priori which goal the user wants to achieve, and must both predict the user's intended goal, and assist in achieving that goal. We formulate the problem of shared autonomy as a Partially Observable Markov Decision Process with uncertainty over the user's goal. We utilize maximum entropy inverse optimal control to estimate a distribution over the user's goal based on the history of inputs. Ideally, the robot assists the user by solving for an action which minimizes the expected cost-to-go for the (unknown) goal. As solving the POMDP to select the optimal action is intractable, we use hindsight optimization to approximate the solution. In a user study, we compare our method to a standard predict-then-blend approach. We find that our method enables users to accomplish tasks more quickly while utilizing less input. However, when asked to rate each system, users were mixed in their assessment, citing a tradeoff between maintaining control authority and accomplishing tasks quickly.