Isaac S. Sheidlower

Orcid: 0009-0004-4487-8357

According to our database1, Isaac S. Sheidlower authored at least 11 papers between 2021 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Human-Interactive Robot Learning: Definition, Challenges, and Recommendations.
ACM Trans. Hum. Robot Interact., March, 2026

How Users Understand Robot Foundation Model Performance through Task Success Rates and Beyond.
CoRR, February, 2026

Investigating User Perceptions of Robot Foundation Model Performance and Evaluations.
Proceedings of the Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, 2026

2024
Towards Interpretable Foundation Models of Robot Behavior: A Task Specific Policy Generation Approach.
CoRR, 2024

Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies.
Proceedings of the 33rd IEEE International Conference on Robot and Human Interactive Communication, 2024

Online Behavior Modification for Expressive User Control of RL-Trained Robots.
Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024

On the Effect of Robot Errors on Human Teaching Dynamics.
Proceedings of the 12th International Conference on Human-Agent Interaction, 2024

2023
Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Tasks.
CoRR, 2023

2022
Keeping Humans in the Loop: Teaching via Feedback in Continuous Action Space Environments.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Environment Guided Interactive Reinforcement Learning: Learning from Binary Feedback in High-Dimensional Robot Task Environments.
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021
When Oracles Go Wrong: Using Preferences as a Means to Explore.
Proceedings of the Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 2021


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