1 code implementation • 21 Feb 2019 • Yilun Zhou, Steven Schockaert, Julie A. Shah
In this paper we instead propose to learn to predict path quality from crowdsourced human assessments.
1 code implementation • WS 2019 • Yilun Zhou, Julie A. Shah, Steven Schockaert
Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment.
1 code implementation • 21 Oct 2018 • Heitor J. Savino, Luciano C. A. Pimenta, Julie A. Shah, Bruno V. Adorno
The dual quaternion algebra is used to model the agents' poses and also in the distributed control laws, making the proposed technique easily applicable to time-varying formation control of general robotic systems.
Robotics Optimization and Control
no code implementations • NeurIPS 2018 • Ankit Shah, Pritish Kamath, Julie A. Shah, Shen Li
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 task.
no code implementations • NeurIPS 2015 • Been Kim, Julie A. Shah, Finale Doshi-Velez
We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection.
no code implementations • NeurIPS 2014 • Chongjie Zhang, Julie A. Shah
We develop a simple linear programming approach and a more scalable game-theoretic approach for computing an optimal fairness policy.
no code implementations • 11 Sep 2019 • Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich, Julie A. Shah
Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 17 Feb 2021 • Sangwon Seo, Lauren R. Kennedy-Metz, Marco A. Zenati, Julie A. Shah, Roger D. Dias, Vaibhav V. Unhelkar
Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors.
no code implementations • 18 Oct 2021 • Shen Li, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, Julie A. Shah
Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models.
no code implementations • 5 Feb 2024 • Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie A. Shah
We observe that how humans behave reveals how they see the world.
no code implementations • 28 Feb 2024 • Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie A. Shah
We describe a framework for using natural language to design state abstractions for imitation learning.