13 citations · 18 across the 5 of their papers we have counts for
7 papers
Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity Demand
Arec Jamgochian, Di Wu, Kunal Menda +2
Some real-world decision-making problems require making probabilistic forecasts over multiple steps at once. However, methods for probabilistic forecasting may fail to capture corr…
Multi-Vehicle Control in Roundabouts using Decentralized Game-Theoretic Planning
Arec Jamgochian, Kunal Menda, Mykel J. Kochenderfer
Safe navigation in dense, urban driving environments remains an open problem and an active area of research. Unlike typical predict-then-plan approaches, game-theoretic planning co…
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual
Kunal Menda, Jayesh K. Gupta, Zachary Manchester +1
Model-based paradigms for decision-making and control are becoming ubiquitous in robotics. They rely on the ability to efficiently learn a model of the system from data. Structured…
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…
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…
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning
Kunal Menda, Katherine Driggs-Campbell, Mykel J. Kochenderfer
While imitation learning is often used in robotics, the approach frequently suffers from data mismatch and compounding errors. DAgger is an iterative algorithm that addresses these…