4 citations · 8 across the 5 of their papers we have counts for
6 papers
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
Kunal Menda, Jean de Becdelièvre, Jayesh K. Gupta +3
System identification is a key step for model-based control, estimator design, and output prediction. This work considers the offline identification of partially observed nonlinear…
Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints
Shushman Choudhury, Jayesh K. Gupta, Mykel J. Kochenderfer +2
We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of…
Structured Mechanical Models for Robot Learning and Control
Jayesh K. Gupta, Kunal Menda, Zachary Manchester +1
Model-based methods are the dominant paradigm for controlling robotic systems, though their efficacy depends heavily on the accuracy of the model used. Deep neural networks have be…
Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
Raunak P. Bhattacharyya, Derek J. Phillips, Changliu Liu +3
Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers. However, it is challenging to capture emergent traffi…
Model Primitive Hierarchical Lifelong Reinforcement Learning
Bohan Wu, Jayesh K. Gupta, Mykel J. Kochenderfer
Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult. Some traditional hierarchical reinforcement learning…
Model-Free Imitation Learning with Policy Optimization
Jonathan Ho, Jayesh K. Gupta, Stefano Ermon
In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations. Existing imitation learning algorithms typi…