no code implementations • 15 Mar 2023 • Yuki Shirai, Devesh K. Jha, Arvind U. Raghunathan
This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interactions with uncertainty in physical properties of the object and the environment.
no code implementations • 6 Jun 2021 • Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres
PYROBOCOP is a lightweight Python-based package for control and optimization of robotic systems described by nonlinear Differential Algebraic Equations (DAEs).
no code implementations • 24 Dec 2020 • Arvind U. Raghunathan, Jeffrey T. Linderoth
To the best of our knowledge, our algorithm is the first exact approach for stability verification of DLCS.
Optimization and Control
no code implementations • 22 Jan 2020 • Patrik Kolaric, Devesh K. Jha, Arvind U. Raghunathan, Frank L. Lewis, Mouhacine Benosman, Diego Romeres, Daniel Nikovski
Motivated by these problems, we try to formulate the problem of trajectory optimization and local policy synthesis as a single optimization problem.
Model-based Reinforcement Learning
reinforcement-learning
+2
no code implementations • 21 Nov 2019 • David Bergman, Teng Huang, Philip Brooks, Andrea Lodi, Arvind U. Raghunathan
The framework considers two sets of decision variables; regular and predicted.
no code implementations • 15 May 2019 • Arvind U. Raghunathan, Anoop Cherian, Devesh K. Jha
To this end, we introduce the Gradient-based Nikaido-Isoda (GNI) function which serves: (i) as a merit function, vanishing only at the first-order stationary points of each player's optimization problem, and (ii) provides error bounds to a stationary Nash point.
no code implementations • 28 Jun 2017 • Srikumar Ramalingam, Arvind U. Raghunathan, Daniel Nikovski
We show that this objective function is submodular.