3 citations · 6 across the 7 of their papers we have counts for
8 papers
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…
DeployBench: Benchmarking LLM Agents for Research Artifact Deployment
Yuanli Wang, Yaoyao Qian, Yue Zhang +8
LLM agents have made rapid progress on software engineering and ML research tasks, but these advances often assume access to a working runnable environment. For research artifacts…
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…
WebGraphEval: Multi-Turn Trajectory Evaluation for Web Agents using Graph Representation
Yaoyao Qian, Yuanli Wang, Jinda Zhang +8
Current evaluation of web agents largely reduces to binary success metrics or conformity to a single reference trajectory, ignoring the structural diversity present in benchmark da…
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…
WHEN TO ACT, WHEN TO WAIT: Modeling the Intent-Action Alignment Problem in Dialogue
Yaoyao Qian, Jindan Huang, Yuanli Wang +5
Dialogue systems often fail when user utterances are semantically complete yet lack the clarity and completeness required for appropriate system action. This mismatch arises becaus…
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…
Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment
Xingtong Liu, Maia Stiber, Jindan Huang +4
Reconstructing accurate 3D surface models of sinus anatomy directly from an endoscopic video is a promising avenue for cross-sectional and longitudinal analysis to better understan…