3 citations · 4 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…