Hi, I’m Sreekar.
Soft Robotics · BioRobotics · Robotic Systems · Machine Learning & AI
I’m an M.S. student in Robotics & Autonomous Systems at Boston University and a Graduate Researcher in
Professor Tommaso Ranzani’s Morphable Biorobotics Lab. My work sits at the intersection of
sensorized soft bioinspired robotics, robust sensing, and machine learning-based control, turning noisy real-world signals into
reliable state estimates and control-ready actuation commands.
I started in Computer Science Engineering (AI) at Amrita, then worked across ROS/UAV autonomy and applied ML research.
During an academic exchange at University of California Riverside, I focused on time-series anomaly detection on scientific gravitational signals (LIGO),
which shaped my research style: careful baselines, rigorous evaluation, and clear failure-mode analysis with multiple iterations and improvements.
Fun fact: I’m a certified Starbucks barista ☕.
Feel free to explore the site for projects and research highlights as well as my CV for more information.
Current work (Thesis)
Real-Time Machine Learning-based control of a underwater Stacked Balloon Actuator (SBA)
- Platform: Multi-DOF SBA with embedded sensing for tip communication and sensing.
- Sensing: synchronized IMU + magnetometer readings, plus Aurora EM tracking for ground-truth pose and dataset alignment.
- Models: two-phase learning pipeline - (1) residual network: (Δx,Δy,Δz) → quaternion, (2) LSTM: (magnetometer + quaternion) → volumes (VA,VB,VC).
- What it enables: reliable open-loop actuation and a foundation for closed-loop control (MPC / model-based RL).
Large repeatable dataset, quaternion prediction, and open-loop infinity (lemniscate) tracking.
At a glance:
- End-to-end: sensing → data pipelines → models → evaluation → control integration.
- Practical ML: clean datasets, reproducible training, measurable gains, and deployment-minded constraints.
- Focus: robust learning under physical constraints for deployable soft robotic systems.
Honest diagnostics and strong experiments, and controllers designed based on ML/AI algorithms.
Timeline
Timeline highlighting my Experience and Education (most recent → oldest).
Hover/tap for GPA/Honors (education) and stack (experience).
Graduate Researcher
Learning-based control of underwater soft robotic actuators (thesis research)
Teaching Assistant (ME 568 · Soft Robotics)
Led lab sessions, mentored teams, supported course delivery
Research Programmer
Biomedical imaging workflows for microglial cell identification
M.S. · Robotics & Autonomous Systems
Specialization: Data Analytics
Advanced Certificate in Engineering (Computer Science)
Academic Exchange, Undergraduate Senior Year
AI Research Intern
LLM/NLP pipelines + benchmarking; applied ML research
Robotics Intern
Quadcopter SITL + ROS-based autonomy integration
B.Tech · Computer Science Engineering (Artificial Intelligence)
First Class with Distinction
11th & 12th Class
Higher Secondary
Secondary School
10th Grade