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Srinivas Devadas

3 accepted papers

2021

Accelerating Robot Dynamics Gradients on a CPU, GPU, and FPGA

RA-L 2021

Computing the gradient of rigid body dynamics is a central operation in many state-of-the-art planning and control algorithms in robotics. Parallel computing platforms such as GPUs and FPGAs can offer performance gains for algorithms with hardware-compatible computational structures. In this letter,

Cited by 38SourceScholar
2020

On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data

ICML 2020poster

In this paper, we consider the problem of designing Differentially Private (DP) algorithms for Stochastic Convex Optimization (SCO) on heavy-tailed data. The irregularity of such data violates some key assumptions used in almost all existing DP-SCO and DP-ERM methods, resulting in failure to provide…

Cited by 71SourcePDFScholar
2019

Benchmarking and Workload Analysis of Robot Dynamics Algorithms

IROS 2019poster

Rigid body dynamics calculations are needed for many tasks in robotics, including online control. While there currently exist several competing software implementations that are sufficient for use in traditional control approaches, emerging sophisticated motion control techniques such as nonlinear m…

Cited by 17SourceScholar