40 citations · 144 across the 31 of their papers we have counts for
35 papers
Deliberate Practice: Learning Robot Skills under a Budget
Shivam Vats, Sudarshan Harithas, Mete Tuluhan Akbulut +2
We consider the problem of autonomously learning robot skills under a limited practice budget for sequential tasks. We propose an active skill learning algorithm, \emph{Deliberate…
Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations
Yuki Shirai, Arvind Raghunathan, Devesh K. Jha
Designing trajectories for manipulation through contact is challenging as it requires reasoning of object \& robot trajectories as well as complex contact sequences simultaneously.…
Constrained Optimization From a Control Perspective via Feedback Linearization
Runyu Zhang, Arvind Raghunathan, Jeff Shamma +1
Tools from control and dynamical systems have proven valuable for analyzing and developing optimization methods. In this paper, we establish rigorous theoretical foundations for us…
Coach Reservation for Groups Requests
Carlos H. Cardonha, Arvind U. Raghunathan
Passenger transportation is a core aspect of a railway company's business, with ticket sales playing a central role in generating revenue. Profitable operations in this context rel…
Constructing Tight Quadratic Relaxations for Global Optimization: II. Underestimating Difference-of-Convex (D.C.) Functions
William R. Strahl, Arvind U. Raghunathan, Nikolaos V. Sahinidis +1
Recent advances in the efficiency and robustness of algorithms solving convex quadratically constrained quadratic programming (QCQP) problems motivate developing techniques for cre…
Constructing Tight Quadratic Relaxations for Global Optimization: I. Outer-Approximating Twice-Differentiable Convex Functions
William R. Strahl, Arvind U. Raghunathan, Nikolaos V. Sahinidis +1
When computing bounds, spatial branch-and-bound algorithms often linearly outer approximate convex relaxations for non-convex expressions in order to capitalize on the efficiency a…