51 citations · 157 across the 10 of their papers we have counts for
12 papers
LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions
Andrew Hundt, Rumaisa Azeem, Masoumeh Mansouri +1
Members of the Human-Robot Interaction (HRI) and Machine Learning (ML) communities have proposed Large Language Models (LLMs) as a promising resource for robotics tasks such as nat…
Love, Joy, and Autism Robots: A Metareview and Provocatype
Andrew Hundt, Gabrielle Ohlson, Pieter Wolfert +3
Previous work has observed how Neurodivergence is often harmfully pathologized in Human-Computer Interaction (HCI) and Human-Robot interaction (HRI) research. We conduct a review o…
Can 'Robots Won't Save Japan' Save Robotics? Reviewing an Ethnography of Eldercare Automation
Andrew Hundt
Imagine activating new robots meant to aid staff in an elder care facility, only to discover the robots are counterproductive. They undermine the most meaningful moments of the job…
Towards Equitable Agile Research and Development of AI and Robotics
Andrew Hundt, Julia Schuller, Severin Kacianka
Machine Learning (ML) and 'Artificial Intelligence' ('AI') methods tend to replicate and amplify existing biases and prejudices, as do Robots with AI. For example, robots with faci…
SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation
Zhixuan Liu, Peter Schaldenbrand, Beverley-Claire Okogwu +5
Accurate representation in media is known to improve the well-being of the people who consume it. Generative image models trained on large web-crawled datasets such as LAION are kn…
Robots Enact Malignant Stereotypes
Andrew Hundt, William Agnew, Vicky Zeng +2
Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6]…
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer
Andrew Hundt, Benjamin Killeen, Nicholas Greene +4
Current Reinforcement Learning (RL) algorithms struggle with long-horizon tasks where time can be wasted exploring dead ends and task progress may be easily reversed. We develop th…
sharpDARTS: Faster and More Accurate Differentiable Architecture Search
Andrew Hundt, Varun Jain, Gregory D. Hager
Neural Architecture Search (NAS) has been a source of dramatic improvements in neural network design, with recent results meeting or exceeding the performance of hand-tuned archite…
Evaluating Methods for End-User Creation of Robot Task Plans
Chris Paxton, Felix Jonathan, Andrew Hundt +2
How can we enable users to create effective, perception-driven task plans for collaborative robots? We conducted a 35-person user study with the Behavior Tree-based CoSTAR system t…
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
Andrew Hundt, Varun Jain, Chia-Hung Lin +2
A robot can now grasp an object more effectively than ever before, but once it has the object what happens next? We show that a mild relaxation of the task and workspace constraint…
User Experience of the CoSTAR System for Instruction of Collaborative Robots
Chris Paxton, Felix Jonathan, Andrew Hundt +2
How can we enable novice users to create effective task plans for collaborative robots? Must there be a tradeoff between generalizability and ease of use? To answer these questions…
CoSTAR: Instructing Collaborative Robots with Behavior Trees and Vision
Chris Paxton, Andrew Hundt, Felix Jonathan +2
For collaborative robots to become useful, end users who are not robotics experts must be able to instruct them to perform a variety of tasks. With this goal in mind, we developed…