17 citations · 31 across the 6 of their papers we have counts for
6 papers
Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations
Yanwei Wang, Nadia Figueroa, Shen Li +2
Learning from demonstration (LfD) has succeeded in tasks featuring a long time horizon. However, when the problem complexity also includes human-in-the-loop perturbations, state-of…
Supervised Bayesian Specification Inference from Demonstrations
Ankit Shah, Pritish Kamath, Shen Li +4
When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that ta…
Interactive Robot Training for Non-Markov Tasks
Ankit Shah, Samir Wadhwania, Julie Shah
Defining sound and complete specifications for robots using formal languages is challenging, while learning formal specifications directly from demonstrations can lead to over-cons…
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example
Serena Booth, Yilun Zhou, Ankit Shah +1
Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response t…
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach
Serena Booth, Ankit Shah, Yilun Zhou +1
Though neural network models demonstrate impressive performance, we do not understand exactly how these black-box models make individual predictions. This drawback has led to subst…
Planning With Uncertain Specifications (PUnS)
Ankit Shah, Shen Li, Julie Shah
Reward engineering is crucial to high performance in reinforcement learning systems. Prior research into reward design has largely focused on Markovian functions representing the r…