31 citations · 178 across the 53 of their papers we have counts for
13 papers · 1 filter
Utilizing Human Feedback for Primitive Optimization in Wheelchair Tennis
Arjun Krishna, Zulfiqar Zaidi, Letian Chen +3
Agile robotics presents a difficult challenge with robots moving at high speeds requiring precise and low-latency sensing and control. Creating agile motion that accomplishes the t…
Safe Inverse Reinforcement Learning via Control Barrier Function
Yue Yang, Letian Chen, Matthew Gombolay
Learning from Demonstration (LfD) is a powerful method for enabling robots to perform novel tasks as it is often more tractable for a non-roboticist end-user to demonstrate the des…
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings
Andrew Silva, Pradyumna Tambwekar, Matthew Gombolay
Federated learning is a training paradigm that learns from multiple distributed users without aggregating data on a centralized server. Such a paradigm promises the ability to depl…
Athletic Mobile Manipulator System for Robotic Wheelchair Tennis
Zulfiqar Zaidi, Daniel Martin, Nathaniel Belles +20
Athletics are a quintessential and universal expression of humanity. From French monks who in the 12th century invented jeu de paume, the precursor to modern lawn tennis, back to t…
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige +2
Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to enable humans to gain insight into the decision-making of machine learning models. Despi…
Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
Letian Chen, Sravan Jayanthi, Rohan Paleja +3
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. However, c…
A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting
Pradyumna Tambwekar, Lakshita Dodeja, Nathan Vaska +2
Many real-world tasks involve a mixed-initiative setup, wherein humans and AI systems collaboratively perform a task. While significant work has been conducted towards enabling hum…
Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees
Esmaeil Seraj, Andrew Silva, Matthew Gombolay
In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been commissioned by researchers to enable accurate, online wildfire coverage and tracking. While the major…
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees
Max Zuo, Logan Schick, Matthew Gombolay +1
Modern day computer games have extremely large state and action spaces. To detect bugs in these games' models, human testers play the games repeatedly to explore the game and find…
Towards Cognitive Robots That People Accept in Their Home
Nina Moorman, Erin Hedlund-Botti, Matthew Gombolay
It is intractable for assistive robots to have all functionalities pre-programmed prior to deployment. Rather, it is more realistic for robots to perform supplemental, on-site lear…
Strategy Discovery and Mixture in Lifelong Learning from Heterogeneous Demonstration
Sravan Jayanthi, Letian Chen, Matthew Gombolay
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. A key chal…
Learning Interpretable, High-Performing Policies for Autonomous Driving
Rohan Paleja, Yaru Niu, Andrew Silva +3
Gradient-based approaches in reinforcement learning (RL) have achieved tremendous success in learning policies for autonomous vehicles. While the performance of these approaches wa…
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming
Sachin Konan, Esmaeil Seraj, Matthew Gombolay
Information sharing is key in building team cognition and enables coordination and cooperation. High-performing human teams also benefit from acting strategically with hierarchical…