42 citations · 66 across the 6 of their papers we have counts for
6 papers
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan Paleja, Letian Chen, Yaru Niu +10
Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…
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…
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…
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…
Action2Vec: A Crossmodal Embedding Approach to Action Learning
Meera Hahn, Andrew Silva, James M. Rehg
We describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video cli…
Effects of Interruptibility-Aware Robot Behavior
Siddhartha Banerjee, Andrew Silva, Karen Feigh +1
As robots become increasingly prevalent in human environments, there will inevitably be times when a robot needs to interrupt a human to initiate an interaction. Our work introduce…