316 citations · 496 across the 17 of their papers we have counts for
19 papers
Iteratively Preconditioned Gradient-Descent Approach for Moving Horizon Estimation Problems
Tianchen Liu, Kushal Chakrabarti, Nikhil Chopra
Moving horizon estimation (MHE) is a widely studied state estimation approach in several practical applications. In the MHE problem, the state estimates are obtained via the soluti…
Cartographer_glass: 2D Graph SLAM Framework using LiDAR for Glass Environments
Lasitha Weerakoon, Gurtajbir Singh Herr, Jasmine Blunt +2
We study algorithms for detecting and including glass objects in an optimization-based Simultaneous Localization and Mapping (SLAM) algorithm in this work. When LiDAR data is the p…
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
Kushal Chakrabarti, Nikhil Chopra
Adaptive gradient methods have become popular in optimizing deep neural networks; recent examples include AdaGrad and Adam. Although Adam usually converges faster, variations of Ad…
Co-Design of Lipschitz Nonlinear Systems
Prasad Vilas Chanekar, Nikhil Chopra
Empirical experiences have shown that simultaneous (rather than conventional sequential) plant and controller design procedure leads to an improvement in performance and saving of…
On Accelerating Distributed Convex Optimizations
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper studies a distributed multi-agent convex optimization problem. The system comprises multiple agents in this problem, each with a set of local data points and an associat…
Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective
Kushal Chakrabarti, Nikhil Chopra
Accelerated gradient-based methods are being extensively used for solving non-convex machine learning problems, especially when the data points are abundant or the available data i…
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the problem of multi-agent distributed linear regression in the presence of system noises. In this problem, the system comprises multiple agents wherein each a…
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the multi-agent distributed linear least-squares problem. The system comprises multiple agents, each agent with a locally observed set of data points, and a co…
Adaptive Tracking Control of Soft Robots using Integrated Sensing Skin and Recurrent Neural Networks
Lasitha Weerakoon, Zepeng Ye, Rahul Subramonian Bama +3
In this paper, we study integrated estimation and control of soft robots. A significant challenge in deploying closed loop controllers is reliable proprioception via integrated sen…
Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the multi-agent linear least-squares problem in a server-agent network. In this problem, the system comprises multiple agents, each having a set of local data…
Preserving Statistical Privacy in Distributed Optimization
Nirupam Gupta, Shripad Gade, Nikhil Chopra +1
We present a distributed optimization protocol that preserves statistical privacy of agents' local cost functions against a passive adversary that corrupts some agents in the netwo…
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the problem of multi-agent distributed optimization. In this problem, there are multiple agents in the system, and each agent only knows its local cost functio…
Privacy of Agents' Costs in Peer-to-Peer Distributed Optimization
Nirupam Gupta, Nikhil Chopra
In this paper, we propose a protocol that preserves (statistical) privacy of agents' costs in peer-to-peer distributed optimization against a passive adversary that corrupts certai…
Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs
Nirupam Gupta, Jonathan Katz, Nikhil Chopra
This paper proposes a privacy protocol for distributed average consensus algorithms on bounded real-valued inputs that guarantees statistical privacy of honest agents' inputs again…
Information-Theoretic Privacy in Distributed Average Consensus
Nirupam Gupta, Jonathan Katz, Nikhil Chopra
We present a distributed average consensus protocol that preserves the privacy of agents' inputs. Unlike the differential privacy mechanisms, the presented protocol does not affect…
Passivity-Based Control of Human-Robotic Networks with Inter-Robot Communication Delays and Experimental Verification
J. Yamauchi, M. W. S. Atman, T. Hatanaka +2
In this paper, we present experimental studies on a cooperative control system for human-robotic networks with inter-robot communication delays. We first design a cooperative contr…
Passivity-Based Distributed Optimization with Communication Delays Using PI Consensus Algorithm
Takeshi Hatanaka, Nikhil Chopra, Takayuki Ishizaki +1
In this paper, we address a class of distributed optimization problems in the presence of inter-agent communication delays based on passivity. We first focus on unconstrained distr…
Decentralized Event-Triggering for Control of Nonlinear Systems
Pavankumar Tallapragada, Nikhil Chopra
This paper considers nonlinear systems with full state feedback, a central controller and distributed sensors not co-located with the central controller. We present a methodology f…
On Event Triggered Tracking for Nonlinear Systems
Pavankumar Tallapragada, Nikhil Chopra
In this paper we study an event based control algorithm for trajectory tracking in nonlinear systems. The desired trajectory is modelled as the solution of a reference system with…