6 papers
How Users Understand Robot Foundation Model Performance through Task Success Rates and Beyond
Isaac Sheidlower, Jindan Huang, James Staley +4
Robot Foundation Models (RFMs) represent a promising approach to developing general-purpose home robots. Given the broad capabilities of RFMs, users will inevitably ask an RFM-base…
CHARM: Considering Human Attributes for Reinforcement Modeling
Qidi Fang, Hang Yu, Shijie Fang +4
Reinforcement Learning from Human Feedback has recently achieved significant success in various fields, and its performance is highly related to feedback quality. While much prior…
How Much Progress Did I Make? An Unexplored Human Feedback Signal for Teaching Robots
Hang Yu, Qidi Fang, Shijie Fang +2
Enhancing the expressiveness of human teaching is vital for both improving robots' learning from humans and the human-teaching-robot experience. In this work, we characterize and t…
Demonstration Sidetracks: Categorizing Systematic Non-Optimality in Human Demonstrations
Shijie Fang, Hang Yu, Qidi Fang +2
Learning from Demonstration (LfD) is a popular approach for robots to acquire new skills, but most LfD methods suffer from imperfections in human demonstrations. Prior work typical…
From "Thumbs Up" to "10 out of 10": Reconsidering Scalar Feedback in Interactive Reinforcement Learning
Hang Yu, Reuben M. Aronson, Katherine H. Allen +1
Learning from human feedback is an effective way to improve robotic learning in exploration-heavy tasks. Compared to the wide application of binary human feedback, scalar human fee…
See What I Mean? Expressiveness and Clarity in Robot Display Design
Matthew Ebisu, Hang Yu, Reuben Aronson +1
Nonverbal visual symbols and displays play an important role in communication when humans and robots work collaboratively. However, few studies have investigated how different type…