7 citations · 8 across the 5 of their papers we have counts for
5 papers
A Generalized Acquisition Function for Preference-based Reward Learning
Evan Ellis, Gaurav R. Ghosal, Stuart J. Russell +2
Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task. Previous works have shown that act…
Batch Active Learning of Reward Functions from Human Preferences
Erdem Bıyık, Nima Anari, Dorsa Sadigh
Data generation and labeling are often expensive in robot learning. Preference-based learning is a concept that enables reliable labeling by querying users with preference question…
Preference Elicitation with Soft Attributes in Interactive Recommendation
Erdem Biyik, Fan Yao, Yinlam Chow +4
Preference elicitation plays a central role in interactive recommender systems. Most preference elicitation approaches use either item queries that ask users to select preferred it…
RoboCLIP: One Demonstration is Enough to Learn Robot Policies
Sumedh A Sontakke, Jesse Zhang, Sébastien M. R. Arnold +5
Reward specification is a notoriously difficult problem in reinforcement learning, requiring extensive expert supervision to design robust reward functions. Imitation learning (IL)…
Active Reward Learning from Online Preferences
Vivek Myers, Erdem Bıyık, Dorsa Sadigh
Robot policies need to adapt to human preferences and/or new environments. Human experts may have the domain knowledge required to help robots achieve this adaptation. However, exi…