Udari Madhushani

Udari Madhushani

PhD Candidate, Princeton University

I am a PhD candidate in Mechanical Engineering at Princeton University, where I am advised by Prof. Naomi Leonard. Before joining Princeton, I completed my undergraduate at University of Peradeniya, Sri Lanka. I am broadly interested in the problems at the intersection of machine learning, control theory and robotics.

Research interests

Research Experience

News

09/2020
Received Britt and Eli Harari Fellowship from the Department of Mechanical and Aerospace Engineering, Princeton University.
08/2020
Finished summer internship (Graduate Intern: AI/Deep Learning for Predictive Analytics) at Siemens.
05/2020
Presented our work "A Dynamic Observation Strategy for Multi-agent Multi-armed Bandit Problem" at ECC 2020.
04/2020
Presented our work "Distributed Learning: Sequential Decision Making in Resource-Constrained Environments" at PML4DC workshop, ICLR 2020.
09/2019
Received Larisse Rosentweig Klein Memorial Award from the Department of Mechanical and Aerospace Engineering, Princeton University.
08/2019
Received a Presidential Award for Scientific Publication from the Sri Lankan National Research Council.
06/2019
Presented our work "Heterogeneous Stochastic Interactions for Multiple Agents in a Multi-armed Bandit Problem" at ECC 2019.
09/2018
Received Martin Summerfield Graduate Fellowship from the Department of Mechanical and Aerospace Engineering, Princeton University.
09/2018
Received Athena-Feron Prize from the Department of Mechanical and Aerospace Engineering, Princeton University.
06/2018
Presented our work "Feedback Regularization and Geometric PID Control for Robust Stabilization of a Planar Three-link Hybrid Bipedal Walking Model" at ACC 2018.
03/2018
Received Elliotte Robinson Little '25 Student Aid Fund Fellowship from the School of Engineering and Applied Science, Princeton University.

Recent Projects

Work in Progress

➟

Multi-robot optimal coverage in unknown fields.

with Maria Santos, Naomi Leonard

➟

Improving sample efficiency of multi-agent \(Q\)-Learning.

with Kenza Hamidouche, Naomi Leonard

➟

Bio-inspired risk aversion in sequential decision making.

with Madeleine Andrews, Simon Levin, Naomi Leonard

➟

A fundamental drawback in natural extensions of UCB to multi-agent multi-armed bandits (Workshop paper).

with Naomi Leonard

Theme: Importance-sampling for data-efficient RL (2020)

☞

Hamiltonian \(Q\)-Learning: Leveraging Importance-sampling for Data Efficient RL

Udari Madhushani, Biswadip Dey, Naomi Ehrich Leonard, Amit Chakraborty

Under Review

Project-page / Pdf / Bibtex

We propose a data efficient modification of the \(Q\)-learning approach which uses Hamiltonian Monte Carlo to compute \(Q\) function for problems with stochastic, high-dimensional dynamics.

Theme: Cost-effective communication protocols for distributed bandits (2019-2020)

We study how agents can minimize communication cost by deciding when and what to communicate depending on the sequence of options they chose.

➟

Broadcast when Exploring: Cost-effective Communication in Distributed Stochastic Bandits

Udari Madhushani, Naomi Ehrich Leonard

In Preparation

➟

Cooperative Bandits: A Class of Communication Protocols with Logarithmic Communication Cost

Udari Madhushani, Naomi Ehrich Leonard

In Preparation

☞

Distributed Learning: Sequential Decision Making in Resource-Constrained Environments

Udari Madhushani, Naomi Ehrich Leonard

PML4DC workshop, ICLR 2020

Project-page / Pdf / Bibtex/ Slides

We design a partial communication protocol that obtains the same order of performance as full communication for a significantly smaller communication cost.

☞

A Dynamic Observation Strategy for Multi-agent Multi-armed Bandit Problem

Udari Madhushani, Naomi Ehrich Leonard

ECC, 2020

Project-page / Pdf / Bibtex / Slides / Video

We propose a new communication protocol for multi-agent multi-armed bandit problem that improves group performance with only a logarithmic communication cost.

Theme: Role of network structure and agent heterogeneity in Multi-agent Bandits (2017-2020)

We study multi-agent multi-armed bandit problem where agents observe their neighbors probabilistically.

➟

Decentralized Stochastic Bandits with Probabilistic Communications

Udari Madhushani, Naomi Ehrich Leonard

In Preparation

☞

Distributed Bandits: Probabilistic Communication on \(d\)-regular Graphs

Udari Madhushani, Naomi Ehrich Leonard

Under Review

Project-page / Pdf / Bibtex

We analyze how agent-based strategies contribute to minimizing group regret under communication failures

☞

Heterogeneous Explore-Exploit Strategies on Multi-Star Networks

Udari Madhushani, Naomi Ehrich Leonard

IEEE Control Systems Letters, 2020

Project-page / Pdf / Bibtex

For distributed bandits with a multi-star communication graph, we show how sampling rules for center agents that favor exploring over exploiting make the information that center agents broadcast to their neighbors more useful and improve group performance.

☞

Heterogeneous Stochastic Interactions for Multiple Agents in a Multi-armed Bandit Problem

Udari Madhushani, Naomi Ehrich Leonard

ECC, 2019

Project-page / Pdf / Bibtex / Slides

We consider the case where each agent observes all its neighbors independently with the same probability. We show that the performance of each agent depends on observation probabilities of its own and its neighbors.

Theme: Geometric Controls for Path Planning (2015-2018)

☞

Feedback Regularization and Geometric PID Control for Robust Stabilization of a Planar Three-link Hybrid Bipedal Walking Model

Lasitha Weerakoon, Udari Madhushani, Sanjeeva Maithripala, Jordan Berg

ACC, 2018

Project-page / Pdf / Bibtex / Slides / Video

We propose a geometric PID controller to stabilize a three-link planar bipedal hybrid dynamic walking robot.

☞

Semi-globally Exponential Trajectory Tracking for a Class of Spherical Robots

Udari Madhushani, Sanjeeva Maithripala, Janaka Wijayakulasooriya, Jordan Berg

Automatica, 2017

Project-page / Pdf / Bibtex

We propose a geometric feedback controller for spherical robots capable of tracking a desired position on an inclined plane, in the presence of parameter uncertainty and uncertainty of the inclination of the rolling surface.

☞

Feedback Regularization and Geometric PID Control for Trajectory Tracking of Mechanical Systems: Hoop Robots on an Inclined Plane

Udari Madhushani, Sanjeeva Maithripala, Jordan Berg

ACC, 2017

Project-page / Pdf / Bibtex / Slides

We propose a geometric control strategy for semi-almost global output tracking for a class of interconnected under actuated mechanical systems.

Teaching

I work as an assistant-in-teaching at Princeton University.

Fall '20
MAE 542 Advanced Dynamics.
Instructor: Naomi Leonard
Spring '20
MAE 502/APC 506 Mathematical Methods of Engineering Analysis.
Instructor: Clarence Rowley
Fall '19
MAE 345/MAE 549 Introduction to Robotics.
Instructor: Anirudha Majumdar