3 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learning
Hang Yu, James Staley, Cheng Xi Tsou +8
Human demonstrations provide dense policy-level information but sometimes lack local precision. Human feedback presents accurate local critiques, but offers sparse evaluations rath…
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…
On the Effect of Robot Errors on Human Teaching Dynamics
Jindan Huang, Isaac Sheidlower, Reuben M. Aronson +1
Human-in-the-loop learning is gaining popularity, particularly in the field of robotics, because it leverages human knowledge about real-world tasks to facilitate agent learning. W…