# Situational Confidence Assistance for Lifelong Shared Autonomy

arXiv: https://arxiv.org/abs/2104.06556 · HTML: https://mlsanity.com/paper/2104.06556/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Shared Autonomy via Deep Reinforcement Learning](https://mlsanity.com/paper/1802.01744/) (2018)
- [Shared Autonomy via Hindsight Optimization](https://mlsanity.com/paper/1503.07619/) (2015)
- [Probabilistically Safe Robot Planning with Confidence-Based Human Predictions](https://mlsanity.com/paper/1806.00109/) (2018)
- [Controlling Assistive Robots with Learned Latent Actions](https://mlsanity.com/paper/1909.09674/) (2019)
- [Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections](https://mlsanity.com/paper/2002.00941/) (2020)

## Built on by (most cited first, 15 of 15)

- [The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types](https://mlsanity.com/paper/2208.10687/) (2022)
- [Inducing Structure in Reward Learning by Learning Features](https://mlsanity.com/paper/2201.07082/) (2022)
- [Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback](https://mlsanity.com/paper/2310.20608/) (2023)
- [Aligning Robot and Human Representations](https://mlsanity.com/paper/2302.01928/) (2023)
- [Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions](https://mlsanity.com/paper/2406.07767/) (2024)
- [Shared Autonomy for Proximal Teaching](https://mlsanity.com/paper/2502.19899/) (2025)
- [Masked IRL: LLM-Guided Reward Disambiguation from Demonstrations and Language](https://mlsanity.com/paper/2511.14565/) (2025)
- [Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Tasks](https://mlsanity.com/paper/2312.05991/) (2023)
- [Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models](https://mlsanity.com/paper/2506.14727/) (2025)
- [SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty](https://mlsanity.com/paper/2603.05111/) (2026)
- [Aligning Robot Representations with Humans](https://mlsanity.com/paper/2205.07882/) (2022)
- [Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies](https://mlsanity.com/paper/2406.13711/) (2024)
- [Online Behavior Modification for Expressive User Control of RL-Trained Robots](https://mlsanity.com/paper/2408.16776/) (2024)
- [QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents](https://mlsanity.com/paper/2511.17855/) (2025)
- [BlenDAgger: Blended Shared Control for Interactive Imitation Learning](https://mlsanity.com/paper/2609.37599/) (2026)

## Abstract (arXiv)

Shared autonomy enables robots to infer user intent and assist in accomplishing it. But when the user wants to do a new task that the robot does not know about, shared autonomy will hinder their performance by attempting to assist them with something that is not their intent. Our key idea is that the robot can detect when its repertoire of intents is insufficient to explain the user's input, and give them back control. This then enables the robot to observe unhindered task execution, learn the new intent behind it, and add it to this repertoire. We demonstrate with both a case study and a user study that our proposed method maintains good performance when the human's intent is in the robot's repertoire, outperforms prior shared autonomy approaches when it isn't, and successfully learns new skills, enabling efficient lifelong learning for confidence-based shared autonomy.