mlsanityData to 14 Oct 2026

Situational Confidence Assistance for Lifelong Shared Autonomy

arXiv 2104.06556 ↗First posted 14 Apr 2021cs.RO cs.HC cs.LGcited by 15

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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.

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

7 of 27 references.

References · 7

  1. 2016Generative Adversarial Imitation Learningcited by 1,597
  2. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406
  3. 2018Shared Autonomy via Deep Reinforcement Learningcited by 66
  4. 2015Shared Autonomy via Hindsight Optimizationcited by 46
  5. 2018Probabilistically Safe Robot Planning with Confidence-Based Human Predictionscited by 28
  6. 2019Controlling Assistive Robots with Learned Latent Actionscited by 28
  7. 2020Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Correctionscited by 19

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 3; 2025: 4; 2026: 2.

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  1. 2022The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Typescited by 26
  2. 2022Inducing Structure in Reward Learning by Learning Featurescited by 16
  3. 2023Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedbackcited by 13
  4. 2023Aligning Robot and Human Representationscited by 11
  5. 2024Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actionscited by 6
  6. 2025Shared Autonomy for Proximal Teachingcited by 4
  7. 2025Masked IRL: LLM-Guided Reward Disambiguation from Demonstrations and Languagecited by 4
  8. 2023Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Taskscited by 1
  9. 2025Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Modelscited by 1
  10. 2026SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertaintycited by 1
  11. 2022Aligning Robot Representations with Humanscited by 0
  12. 2024Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policiescited by 0
3 more
  1. 2024Online Behavior Modification for Expressive User Control of RL-Trained Robotscited by 0
  2. 2025QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agentscited by 0
  3. 2026BlenDAgger: Blended Shared Control for Interactive Imitation Learningcited by 0