- Towards Interpretable Foundation Models of Robot Behavior: A Task Specific Policy Generation Approach
Isaac Sheidlower, Reuben Aronson, Elaine Short
RLC 2024, Training Agents with Foundation Models (TAFM) Workshop, https://arxiv.org/abs/2407.08065
- Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies
Isaac Sheidlower, Emma Bethel, Douglas Lilly, Reuben Aronson, Elaine Short
IEEE RO-MAN 2024, Conference Paper, https://arxiv.org/pdf/2406.13711
- Online Behavior Modification for Expressive User Control of RL-Trained Robots
Isaac Sheidlower, Mavis Murdock, Emma Bethel, Reuben Aronson, Elaine Short
HRI 2024, Conference Paper, https://dl.acm.org/doi/abs/10.1145/3610977.3634947
- Modifying RL Policies with Imagined Actions: How Predictable Policies can Enable Users to Perform Novel Tasks
Isaac Sheidlower, Reuben Aronson, Elaine Short
AAAI Fall Symposium: AI-HRI, Short Contributions Paper, https://ai-hri.github.io/2023/papers/FSS-23_paper_9464_cr.pdf
- Keeping Humans in the Loop: Teaching via Feedback in Continuous Action Space Environments
Isaac Sheidlower, Allison Moore, Elaine Short
IROS 2022, Conference Paper, https://ieeexplore.ieee.org/document/9982282
- Environment Guided Interactive Reinforcement Learning: Learning from Binary Feedback in High-Dimensional Robot Task Environments
Isaac Sheidlower, Allison Moore, Elaine Short
AAMAS 2022, Extended Abstract, https://dl.acm.org/doi/10.5555/3535850.3536090
- When Oracles Go Wrong: Using Preferences as a Means to Explore
Isaac Sheidlower, Elaine Short
HRI 2021, Late Breaking Report (LBR), https://dl.acm.org/doi/10.1145/3434074.3447189
Nominated for best LBR*
