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Princeton University

• PhD, Mechanical and Aerospace Engineering

Duke University

• BSE, Mechanical Engineering
• BS, Physics
• Certificate, Innovation and Entrepreneurship

Princeton Intelligent Robot Motion Lab

Anirudha Majumdar

• My research aims to approach robotics through a fundamental science’s perspective. My work applies concepts and theories in information theory to quantify performance of robotic systems with arbitrary dynamics, tasks, and control strategies, given their sensing capabilities and computational constraints. I am also interested in applying these theories in the design and implementation of robotics.

Duke Pratt Fellows

Stefan Goetz

• Developed theories and simulations on control and optimization of lattice modular multilevel converters with serial and parallel connectivity. Obtained most efficient control algorithms for lattice converters under requirements (converter size, input/output terminals, and output voltage/current).

Duke Neutrino & Cosmology Group

Kate Scholberg

• Worked on SNEWS (Supernova Early Warning System), produced sky maps with predicted supernova location and uncertainty intervals. Analyzed neutrino events detected at the Super-Kamiokande Detector.

DukeEngage in Detroit

Consulting Intern, TechTown Detroit

• Analyzed TechTown programs via interviews with program directors and clients.
• Provided suggestions for improving each program as well as recommendations for potential collaborations across programs.
• Served as peer mentor for 12 student entrepreneurs for Launch Detroit, a summer entrepreneurship camp for technology-oriented college students.

Duke Motorsports Team

Wheels Subsystem Lead

• Engaged other students to build and assemble wheels for the car competing in 2020 and 2021.
• Designed, modified, and performed analysis on the wheels for the car competing in 2021.

Fundamental Limits for Sensor-Based Robot Control

• Our goal is to develop theory and algorithms for establishing fundamental limits on performance for a given task imposed by a robot's sensors. In order to achieve this, we define a quantity that captures the amount of task-relevant information provided by a sensor. Using a novel version of the generalized Fano inequality from information theory, we demonstrate that this quantity provides an upper bound on the highest achievable expected reward for one-step decision making tasks. We then extend this bound to multi-step problems via a dynamic programming approach. We present algorithms for numerically computing the resulting bounds, and demonstrate our approach on three examples, (i) the lava problem from the literature on partially observable Markov decision processes, (ii) an example with continuous state and observation spaces corresponding to a robot catching a freely-falling object, and (iii) obstacle avoidance using a depth sensor with non-Gaussian noise. We demonstrate the ability of our approach to establish strong limits on achievable performance for these problems by comparing our upper bounds with achievable lower bounds (computed by synthesizing or learning concrete control policies).

• Francis Robins Upton Fellowship (2022)
• Phillips Fellowship (2023)

• Dean's List with Distinction (2018, 2019, 2021)
• Engineering Honor Societies: Tau Beta Pi, Pi Tau Sigma
• Graduation with Distinction (2022)