1 code implementation • 8 Jan 2022 • Arec Jamgochian, Di wu, Kunal Menda, Soyeon Jung, Mykel J. Kochenderfer
In this paper, we introduce the conditional approximate normalizing flow (CANF) to make probabilistic multi-step time-series forecasts when correlations are present over long time horizons.
1 code implementation • 5 May 2021 • Kunal Menda, Jayesh K. Gupta, Zachary Manchester, Mykel J. Kochenderfer
Structured Mechanical Models (SMMs) are a data-efficient black-box parameterization of mechanical systems, typically fit to data by minimizing the error between predicted and observed accelerations or next states.
1 code implementation • ICML 2020 • Kunal Menda, Jean de Becdelièvre, Jayesh K. Gupta, Ilan Kroo, Mykel J. Kochenderfer, Zachary Manchester
System identification is a key step for model-based control, estimator design, and output prediction.
1 code implementation • L4DC 2020 • Jayesh K. Gupta, Kunal Menda, Zachary Manchester, Mykel J. Kochenderfer
Deep neural networks have been used to learn models of robot dynamics from data, but they suffer from data-inefficiency and the difficulty to incorporate prior knowledge.
no code implementations • 25 Sep 2019 • Kunal Menda, Jean de Becdelièvre, Jayesh K Gupta, Ilan Kroo, Mykel J. Kochenderfer, Zachary Manchester
System identification is the process of building a mathematical model of an unknown system from measurements of its inputs and outputs.
1 code implementation • 22 Feb 2019 • Jayesh K. Gupta, Kunal Menda, Zachary Manchester, Mykel J. Kochenderfer
We address the need for a flexible, gray-box model of mechanical systems that can seamlessly incorporate prior knowledge where it is available, and train expressive function approximators where it is not.
Model-based Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 22 Jul 2018 • 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.
1 code implementation • 19 Sep 2017 • Kunal Menda, Yi-Chun Chen, Justin Grana, James W. Bono, Brendan D. Tracey, Mykel J. Kochenderfer, David Wolpert
The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such decision problems.
no code implementations • 18 Sep 2017 • Kunal Menda, Katherine Driggs-Campbell, Mykel J. Kochenderfer
While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors.