31 citations · 178 across the 53 of their papers we have counts for
7 papers · 1 filter
When Your Robot Breaks: Active Learning During Plant Failure
Mariah Schrum, Matthew Gombolay
Detecting and adapting to catastrophic failures in robotic systems requires a robot to learn its new dynamics quickly and safely to best accomplish its goals. To address this chall…
Learning to Dynamically Coordinate Multi-Robot Teams in Graph Attention Networks
Zheyuan Wang, Matthew Gombolay
Increasing interest in integrating advanced robotics within manufacturing has spurred a renewed concentration in developing real-time scheduling solutions to coordinate human-robot…
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
Rohan Paleja, Andrew Silva, Letian Chen +1
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource cons…
Safe Coordination of Human-Robot Firefighting Teams
Esmaeil Seraj, Andrew Silva, Matthew Gombolay
Wildfires are destructive and inflict massive, irreversible harm to victims' lives and natural resources. Researchers have proposed commissioning unmanned aerial vehicles (UAVs) to…
Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration
Rohan Paleja, Matthew Gombolay
For assistive robots and virtual agents to achieve ubiquity, machines will need to anticipate the needs of their human counterparts. The field of Learning from Demonstration (LfD)…
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
Andrew Silva, Taylor Killian, Ivan Dario Jimenez Rodriguez +2
Decision trees are ubiquitous in machine learning for their ease of use and interpretability. Yet, these models are not typically employed in reinforcement learning as they cannot…
Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning
Andrew Silva, Matthew Gombolay
Deep reinforcement learning has been successful in a variety of tasks, such as game playing and robotic manipulation. However, attempting to learn \textit{tabula rasa} disregards t…