activity
20192022
most citedOptimization Based Planner Tracker Design for Safety Guarantees

7 citations · 21 across the 7 of their papers we have counts for

collaborators

13 papers

eess.SY20221 cited

Stochastic MPC with Realization-Adaptive Constraint Tightening

Hotae Lee, Monimoy Bujarbaruah, Francesco Borrelli

This paper presents a stochastic model predictive controller (SMPC) for linear time-invariant systems in the presence of additive disturbances. The distribution of the disturbance…

eess.SY2021

A Simple Robust MPC for Linear Systems with Parametric and Additive Uncertainty

Monimoy Bujarbaruah, Ugo Rosolia, Yvonne R. Stürz +1

We propose a simple and computationally efficient approach for designing a robust Model Predictive Controller (MPC) for constrained uncertain linear systems. The uncertainty is mod…

cs.RO2021

Learning Environment Constraints in Collaborative Robotics: A Decentralized Leader-Follower Approach

Monimoy Bujarbaruah, Yvonne R. Stürz, Conrad Holda +2

In this paper, we propose a leader-follower hierarchical strategy for two robots collaboratively transporting an object in a partially known environment with obstacles. Both robots…

eess.SY2020

Learning How to Solve Bubble Ball

Hotae Lee, Monimoy Bujarbaruah, Francesco Borrelli

"Bubble Ball" is a game built on a 2D physics engine, where a finite set of objects can modify the motion of a bubble-like ball. The objective is to choose the set and the initial…

cs.RO2020

Learning to Play Cup-and-Ball with Noisy Camera Observations

Monimoy Bujarbaruah, Tony Zheng, Akhil Shetty +2

Playing the cup-and-ball game is an intriguing task for robotics research since it abstracts important problem characteristics including system nonlinearity, contact forces and pre…

eess.SY20197 cited

Near-Optimal Rapid MPC using Neural Networks: A Primal-Dual Policy Learning Framework

Xiaojing Zhang, Monimoy Bujarbaruah, Francesco Borrelli

In this paper, we propose a novel framework for approximating the explicit MPC policy for linear parameter-varying systems using supervised learning. Our learning scheme guarantees…