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Shared Autonomy via Hindsight Optimization

arXiv 1503.07619 ↗First posted 26 Mar 2015cs.ROcited by 46

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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.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2015.

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Citing papers per year: 2020: 1; 2021: 10; 2022: 11; 2023: 5; 2024: 6; 2025: 5; 2026: 8.

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  1. 2021Collaborating with Humans without Human Datacited by 139
  2. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 101
  3. 2020Active Preference-Based Gaussian Process Regression for Reward Learningcited by 70
  4. 2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 50
  5. 2021On the Importance of Environments in Human-Robot Coordinationcited by 37
  6. 2022The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Modelscited by 31
  7. 2022On the Sensitivity of Reward Inference to Misspecified Human Modelscited by 25
  8. 2023To the Noise and Back: Diffusion for Shared Autonomycited by 20
  9. 2022PATO: Policy Assisted TeleOperation for Scalable Robot Data Collectioncited by 19
  10. 2021Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objectscited by 17
  11. 2021Situational Confidence Assistance for Lifelong Shared Autonomycited by 15
  12. 2022Conditional Imitation Learning for Multi-Agent Gamescited by 13
34 more
  1. 2022Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddingscited by 12
  2. 2023Active Reward Learning from Online Preferencescited by 12
  3. 2022Intention estimation from gaze and motion features for human-robot shared-control object manipulationcited by 11
  4. 2023Gaze-based intention estimation: principles, methodologies, and applications in HRIcited by 11
  5. 2024Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisitioncited by 10
  6. 2025AssistanceZero: Scalably Solving Assistance Gamescited by 10
  7. 2021Analyzing Human Models that Adapt Onlinecited by 8
  8. 2021Learning Altruistic Behaviours in Reinforcement Learning without External Rewardscited by 8
  9. 2022ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learningcited by 8
  10. 2023Surrogate Assisted Generation of Human-Robot Interaction Scenarioscited by 8
  11. 2025MILE: Model-based Intervention Learningcited by 7
  12. 2021Toward AI Assistants That Let Designers Designcited by 6
  13. 2022Zero-Shot Assistance in Sequential Decision Problemscited by 4
  14. 2021On Optimizing Interventions in Shared Autonomycited by 3
  15. 2024Shared Autonomy with IDA: Interventional Diffusion Assistancecited by 3
  16. 2022Collaborative Decision Making Using Action Suggestionscited by 2
  17. 2022Towards Cooperative Flight Control Using Visual-Attentioncited by 2
  18. 2025FlashBack: Consistency Model-Accelerated Shared Autonomycited by 2
  19. 2021Disentangled Sequence Clustering for Human Intention Inferencecited by 1
  20. 2023Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Taskscited by 1
  21. 2025Learning Multimodal AI Algorithms for Amplifying Limited User Input into High-dimensional Control Spacecited by 1
  22. 2025SIL: Symbiotic Interactive Learning for Language-Conditioned Human-Agent Co-Adaptationcited by 1
  23. 2026SAPS: Shared Autonomy for Policy Steering by Blending Teleoperation with a Pretrained VLAcited by 1
  24. 2026Visual Verification Enables Inference-time Steering and Autonomous Policy Improvementcited by 1
  25. 2021Leveraging Experience in Lazy Searchcited by 0
  26. 2024Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teamingcited by 0
  27. 2024Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policiescited by 0
  28. 2024Online Behavior Modification for Expressive User Control of RL-Trained Robotscited by 0
  29. 2026End-to-end Optimization of Belief and Policy Learning in Shared Autonomy Paradigmscited by 0
  30. 2026Proximal State Nudging: Reducing Skill Atrophy from AI Assistancecited by 0
  31. 2026HITL-D: Human In The Loop Diffusion Assisted Shared Controlcited by 0
  32. 2026Hierarchical Generative Agents for Simulating Sequential Human Behaviorcited by 0
  33. 2026Steering Autoregressive Vision-Language-Action Policies via Action Token Interventioncited by 0
  34. 2026Training-Free Action Correction for VLA Model Failures via Language Feedbackcited by 0