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Adaptive Monte Carlo Localization for Mobile Robots
Local and global localization are of utmost importance in mobile robotics and play a crucial role in a robot going from remote-controlled to completely autonomous. A probabilistic localizationExpand
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Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation
TLDR
Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. Expand
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Personalized Apprenticeship Learning from Heterogeneous Decision-Makers
TLDR
We propose a personalized apprenticeship learning framework that automatically infers a representation of all human task demonstrators by extracting a human-specific embedding. Expand
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Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
TLDR
We propose a personalized and interpretable apprenticeship scheduling algorithm that infers an interpretable representation of all human task demonstrators by extracting decision-making criteria via an inferred, personalized embedding non-parametric in the number of demonstrator types. Expand
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Heterogeneous Learning from Demonstration
TLDR
The development of human-robot systems able to leverage the strengths of both humans and robotic counterparts has been greatly sought after because of the foreseen, broad-ranging impact across industry and research. Expand
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Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration
TLDR
We propose a Bayesian LfD framework to infer an integrated representation of all human task demonstrators by inferring human-specific embeddings, thereby distilling their unique characteristics. Expand
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Learning from Suboptimal Demonstration via Self-Supervised Reward Regression
TLDR
Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration. Expand
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