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The Limits of Robot Moderators: Evidence Against Robot Personalization and Participation Equalization in a Building Task
2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)
Year: 2024 | Conference Paper |
Prior research has suggested that equalizing participation may benefit group performance and group cohesion. Robot-moderated groups have largely focused on improving member participation by focusing on the least performing member and do not consider the frequency of interaction or type of interaction. We introduce a robot moderator that varies its frequency and interaction types to observe the imp...Show More
Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies
Isaac Sheidlower;Emma Bethel;Douglas Lilly;Reuben M. Aronson;Elaine Schaertl Short
2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)
Year: 2024 | Conference Paper |
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. Given a robot trained with Reinforcement Learning (RL), a user may wish to leverage that autonomy along with their familiarity of how they expect the robot to behave to collaborate with the robot. One technique is for the us...Show More
Enhancing Users’ Predictions of Robotic Pouring Behaviors Using Augmented Reality: A Case Study
2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)
Year: 2024 | Conference Paper |
People effortlessly manipulate fluids due to their learned understanding of fluid dynamics, while robots struggle with complex fluid dynamic calculations, particularly in tasks like pouring. To enhance assistive robots in such tasks, we propose involving users in correcting and providing feedback by visualizing the planned pouring trajectories before they are executed. This paper investigates whet...Show More
How Much Progress Did I Make? An Unexplored Human Feedback Signal for Teaching Robots
Hang Yu;Qidi Fang;Shijie Fang;Reuben M. Aronson;Elaine Schaertl Short
2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)
Year: 2024 | Conference Paper |
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 test a little-used teaching signal: progress, designed to represent the completion percentage of a task. We conducted two online studies with 76 crowd-sourced participants and one public space study with 40 non-expert par...Show More
Intentional User Adaptation to Shared Control Assistance
2024 19th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Shared control approaches to robot assistance, which predict a user’s goal based on their control input and provide autonomous assistance towards the predicted goal, typically assume that user behavior remains the same despite the presence of the assistance and rely on this assumption to infer user goals. However, people operating assisted systems continuously observe the robot behaving differentl...Show More
Online Behavior Modification for Expressive User Control of RL-Trained Robots
Isaac Sheidlower;Mavis Murdock;Emma Bethel;Reuben M. Aronson;Elaine Schaertl Short
2024 19th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
Year: 2024 | Conference Paper |
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 robot has been deployed. To address this, we introduce the idea of online behavior modification, a paradigm in which users have control over behavior features of a robot in real-time as it autonomously completes a task ...Show More
Modeling Variation in Human Feedback with User Inputs: An Exploratory Methodology
2024 19th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
Year: 2024 | Conference Paper |
To expedite the development process of interactive reinforcement learning (IntRL) algorithms, prior work often uses perfect oracles as simulated human teachers to furnish feedback signals. These oracles typically derive from ground-truth knowledge or optimal policies, providing dense and error-free feedback to a robot learner without delay. However, this machine-like feedback behavior fails to acc...Show More
From “Thumbs Up” to “10 out of 10”: Reconsidering Scalar Feedback in Interactive Reinforcement Learning
Hang Yu;Reuben M. Aronson;Katherine H. Allen;Elaine Schaertl Short
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
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 feedback has been used less because it is believed to be noisy and unstable. In this paper, we compare scalar and binary feedback, and demonstrate that scalar feedback benefits learning when properly handled. We collected b...Show More
Inferring Goals with Gaze during Teleoperated Manipulation
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2021 | Conference Paper |
Cited by: Papers (8)
Assistive robot manipulators help people with upper motor impairments perform tasks by themselves. However, teleoperating a robot to perform complex tasks is difficult. Shared control algorithms make this easier: these algorithms predict the user’s goal, autonomously generate a plan to accomplish the goal, and fuse that plan with the user’s input. To accurately predict the user’s goal, these algor...Show More
A Survey of Automated Threaded Fastening
Zhenzhong Jia;Ankit Bhatia;Reuben M. Aronson;David Bourne;Matthew T. Mason
Threaded fasteners are prevalent throughout modern manufacturing. Thus, as the demand for automation in manufacturing increases, so does the demand for automated threaded fastening systems. However, many fundamental issues and engineering challenges still hinder robustness in automation, particularly for smaller screws and critical product finishing requirements. This paper surveys the state of th...Show More
Eye-Hand Behavior in Human-Robot Shared Manipulation
Reuben M. Aronson;Thiago Santini;Thomas C. Kübler;Enkelejda Kasneci;Siddhartha Srinivasa;Henny Admoni
2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
Year: 2018 | Conference Paper |
Cited by: Papers (7)
Shared autonomy systems enhance people’s abilities to perform activities of daily living using robotic manipulators. Recent systems succeed by first identifying their operators’ intentions, typically by analyzing the user’s joystick input. To enhance this recognition, it is useful to characterize people’s behavior while performing such a task. Furthermore, eye gaze is a rich source of information ...Show More
Sensor Selection and Stage & Result Classifications for Automated Miniature Screwdriving
Xianyi Cheng;Zhenzhong Jia;Ankit Bhatia;Reuben M. Aronson;Matthew T. Mason
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2018 | Conference Paper |
Cited by: Papers (5)
Hundreds of billions of small screws are assembled in consumer electronics industry every year, yet reliably automating the screwdriving process remains one of the most challenging tasks. Two barriers to further adoption of robotic threaded fastening systems are system cost and technical challenges, especially for small screws. An affordable intelligent screwdriving system that can support online ...Show More
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