Full-Stack Robotics Engineer

Hi, I’m Kieran

I build robots end to end: mechanical design, embedded hardware, control software, and the AI on top.

First of all… Hello!

I'm a Master's graduate from UC Berkeley who thrives on using engineering to solve real problems.

Everything I build starts with a real problem: from the largest robotic security fleet in the US, deployed to keep communities safe, to a swarm of ocean sensors that optimize global shipping routes.

Working across the entire stack alongside sharp people, on some of the hardest problems out there, is the part I enjoy most.

Experience

Skills & tools

ROS 2

C++

Python

MATLAB

Simulink

Fusion 360

Berkeley Capstone Project · Project overview

CaptAIn: Building a network of autonomous ocean drones

Aerial view of a winding mountain road tracing an energy-efficient path versus a straight line — Roads wind uphill to save energy. Ships should steer around the waves, not straight through them.

Cargo ships waste ~30% of fuel fighting waves. We built Google Maps for the ocean to help them save fuel.

Wave resistance wastes up to ~30% of a cargo ship’s fuel, about 1% of all global emissions.
Forecasting waves lets ships steer smarter, cutting fuel use by ~20% (≈ $9B a year).
Our network of autonomous sailboats maps ocean currents in real time, steering cargo ships clear of high-wave waters.

Swarm mesh network diagram showing sailboats spaced 200 m apart

Fleet of autonomous sail drones on the water

Creating the path-planning and control algorithms in ROS2

The ROS2 node graph I built: waypoints and live sensor data flow through path planning out to the rudder and sail servos.

These boats run on wind, not a propeller, and you can't sail straight upwind, so my ROS2 planner has to tack (zig-zag) to reach any target:

Beating upwind by tacking across the wind
Dynamic waypoint tracking
Real-time wind and heading adjustments

Kieran on the Berkeley Marina pier with an autonomous sail drone

The result: my planner sailed the boat anywhere we pointed it.

Proven on a 2.3 km autonomous round trip up SF Bay, both upwind and downwind. 50 built and tested.

CaptAIn · Field testing

Field Testing & Sensor Improvement

Boat simulation GUI showing commanded rudder and sail angles — Replay-driven boat simulator: rudder & sail

Our testing process was expensive and time-consuming.

We were heavily dependent on tidal windows and the marina's opening hours.
Although field testing was the best way to learn how the boat really behaved, we needed a way to rapidly test new algorithm features.
I used our pre-existing data from prior tests to create a virtual testing environment which simulated exactly how new iterations on the control algorithm would react to real-world data.
This let me and the team conduct hardware-in-the-loop testing in the lab, catching actuation bugs before any on-water run.
This reduced the iteration time from days to hours.

Field-testing setup with the autonomous boat at the Berkeley Marina

Our sensor measurements were too noisy to control the boat.

The cheap IMUs we used, combined with the boat rocking in every wave, left the raw motion data noisy and hard to trust.
I implemented a 1-D Kalman filter to fuse the readings and sharpen sensor confidence.
Field testing showed an 85% drop in steady-state sensor noise (RMS).

Steady-state IMU noise: raw signal versus low-pass + 1-D Kalman — Real field data: the Kalman filter pulling wave noise out of a steady-state signal

Kalman filter response to a spontaneous stimulus — How the Kalman filter responds to a sudden, spontaneous disturbance

Holding our competition rocket, with the CanSat payload nose section at the top — Built entirely from scratch: we founded the team, then designed, built and flew this rocket in under a year.

2nd place at the Mach22 national competition.

Our inaugural entry, beating teams who'd been building their rockets for up to 3 years.

UCL Rocketry · Head of Payload

CanSat Payload: 360° Flight Video & In-Flight Air-Quality Capture

The goal

Build a CanSat that records 360° footage of our rocket's flight.
Collect pollutant and atmospheric data: a rapid, recoverable alternative to weather balloons.

Key contributions:

Led a team of 5 engineers to build a CanSat, launched to 1.5 km and safely recovered.
Owned the CAD designs and the mechanisms for parachute & 360° camera deployment.

CanSat payload CAD with recovery lifting eye — From CAD to flight hardware: the 360° GoPro, avionics stack and recovery gear packed into a 3D-printed shell.

Avionics stack and wiring packed inside the payload — From CAD to flight hardware: the 360° GoPro, avionics stack and recovery gear packed into a 3D-printed shell.

The camera shook too hard to film, so we designed a spring-catch to stabilise it

Lab testing showed the camera would shake too violently for viable footage. We designed a spring-catch mechanism that locks the GoPro steady once the payload deploys.

As the payload deploys, an outer tube displaces a spring…
…driving a stopper that catches and fixes the camera at 45°.
Vibration-absorbing felt on the stopper soaks up the shake, giving a stable, usable 360° shot.

CAD section of the spring-catch: a stopper and an outer tube that displaces the spring — The catch: a stopper and an outer tube that displaces the spring.

Spring-catch undeployed: the GoPro rests against the inner tube surface — Undeployed: GoPro resting on the inner tube surface.

Spring-catch deployed and mounted under the payload — Deployed: stoppers extended, camera fixed and stabilised at 45°.

My proudest project. I helped start UCL Rocketry independently after the university declined to back us. It has since grown into UCL's most successful engineering team, with podium finishes at Spaceport America and several European competitions.

Berkeley Robotics and Human Engineering Laboratory - Quadrupedal running robot

BladeRunner: Training a quadruped to run on spring blades

Creating an energy efficient running quadruped for search and rescue.

Quadruped gait biomimicry: diagonal synchronization versus front-and-back leaping at higher speeds — At higher speeds, quadrupeds shift from diagonal sync to a front-and-back leaping gait, the motion we optimized for.

From blade physics to a trained running policy:

We analysed how spring running blades store and return energy, the same prosthetic design used in Paralympic sprinting.
We modelled a quadruped in MATLAB Simscape Multibody with J-shaped running blades on each leg, capturing compression, joint dynamics, and ground contact.
We trained a DDPG actor-critic agent to run as efficiently as possible on those blades, tuning the reward function to encourage cheetah-like gait cycles from our biomechanics research.

BladeRunner quadruped model in MATLAB Simscape Multibody — The Simscape Multibody environment I used to train the RL policy, with spring-blade compression, joint dynamics, and ground contact fully modelled.

Training locomotion with deep reinforcement learning (DDPG)

A DDPG actor-critic agent learns optimal joint torques through continuous interaction with the Simscape simulation, rather than following a predefined gait.

Engineering principles: Not just setting velocity reward high!

Biomimicry for Stability: Animals maintain stable body posture during running.
Efficient Torque Management: Penalized abrupt joint direction changes to enhance torque efficiency.
Dynamic Ground Contact Control: Minimized ground contact time by penalizing prolonged contact.
Animal Gait Synchronization: Constrained front and back legs to move in phase.