7 citations · 11 across the 13 of their papers we have counts for
14 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…
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…
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…
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…
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…
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…
Control-Theoretic Analysis of Shared Control Systems
Reuben M. Aronson, Elaine Schaertl Short
Users of shared control systems change their behavior in the presence of assistance, which conflicts with assumpts about user behavior that some assistance methods make. In this pa…
Online Behavior Modification for Expressive User Control of RL-Trained Robots
Isaac Sheidlower, Mavis Murdock, Emma Bethel +2
Reinforcement Learning (RL) is an effective method for robots to learn tasks. However, in typical RL, end-users have little to no control over how the robot does the task after the…
Towards Interpretable Foundation Models of Robot Behavior: A Task Specific Policy Generation Approach
Isaac Sheidlower, Reuben Aronson, Elaine Schaertl Short
Foundation models are a promising path toward general-purpose and user-friendly robots. The prevalent approach involves training a generalist policy that, like a reinforcement lear…
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…
Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies
Isaac Sheidlower, Emma Bethel, Douglas Lilly +2
It is crucial that users are empowered to take advantage of the functionality of a robot and use their understanding of that functionality to perform novel and creative tasks. Give…
Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Tasks
Isaac Sheidlower, Reuben Aronson, Elaine Short
It is crucial that users are empowered to use the functionalities of a robot to creatively solve problems on the fly. A user who has access to a Reinforcement Learning (RL) based r…
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…
HARMONIC: A Multimodal Dataset of Assistive Human-Robot Collaboration
Benjamin A. Newman, Reuben M. Aronson, Siddartha S. Srinivasa +2
We present the Human And Robot Multimodal Observations of Natural Interactive Collaboration (HARMONIC) data set. This is a large multimodal data set of human interactions with a ro…