bencobley.com
Hey, I'm Ben!
I am an engineer & maker. I work at the intersection of hardware and software, in the sweet spot between a designer /
developer / creative technologist. Playing the wildcard role in diverse teams suits my interdisciplinary skill set.
I studied at Imperial College, and interned at Google X, Dyson & Brompton. I got to join some great
projects. More recently, I worked on the world’s waste problem at TrueCircle AI.
I am due to start a new role in the climate-tech space at the end of June, stay tuned!
Outside work, I love making things with friends. I am currently taking some time off to travel in the van I
converted, but I'm always available to chat. Say hello!.
TrueCircle
One Year Full-Time
@ TrueCircle HQ, London
Mechanical
Prototyping
Embedded systems
Strategy
Highlights:
- First full-time engineer on hardware team
- Launched MVP prototype into UK facility
- Scaled product to 30+ facilities internationally
With thanks to PH et al.
TrueCircle AI
Design Engineer @ Early-Stage Startup
How can we leverage AI to improve plastic recovery in recycling facilities?
TrueCircle AI is developing innovative computer vision hardware, retrofitted onto existing
conveyor belts, that captures continuous footage of material streams and calculates
composition by weight in real-time with 95%+ accuracy. The composition data is displayed in intuitive dashboards, enabling
facilities to optimise their flow and prevent valuable recyclable material from going to waste.
We joined forces with UK facilities to explore data-driven optimisation of recycling processes. Our target audience is Plant
Managers, who are focussed on maintaining unreliable equipment and have limited time to make process improvements for better
efficiency. They lacked the data to guide equipment upgrades or set the right price for recycled material. Existing solutions in the
market were not accurate enough and time-consuming/expensive to set up. TrueCircle AI offers a simple and reliable solution
to this problem.
We started with the minimum viable implementation: a GoPro taped above a conveyor belt. Over the following year, I helped develop the
hardware into a finished product. Working in a small, dynamic team enabled significant variety and responsibility in my role. Some
highlights included:
- Launching TrueCircle’s minimum viable hardware prototype and leading the pilot installation at a UK recycling facility.
- Building standard procedures for small-scale manufacture and assembly in-house.
- Leading development of our 'second-gen' hardware, eliminating key failure modes and saving £1000s in maintenance costs.
-
Establishing processes to hand over system installations to a 3rd-party supplier, proving scalability, a key requirement
for TrueCircle’s Series A.
At the end of our first year, we launched an updated hardware product that can be
retrofitted in just a few hours, with zero upfront cost for our customers. Our system sends instant alerts for operational
issues and verifies material purity with 95%+ accuracy in over 30 facilities internationally. This led to increased trust in
material quality and a direct increase in revenue per tonne for customers. To further drive efficiency, TrueCircle introduced Trade.
In this online marketplace, Plant Managers can buy and sell material with purity verified by AI for the first time.
Google X
Six Months Full-Time
@ Google X HQ, California
Embedded systems
Python
Interfaces
PCB design
Highlights:
- Championed an all-new hardware generation
- Collaborated on Mineral, Google's Farming Robot
- Filed 2 Patent Applications
With thanks to RM & RG.
Google X
Intern @ Google's Moonshot Factory
How can we find radical solutions to some of the world's most intractable problems? A paid six-month placement at
Google X; Alphabet's experimental R&D facility. X's 'moonshot' approach to problem-solving
explores ambitious high-risk, high-reward projects. By taking a radical, outside-the-box thinking approach to engineering challenges,
X aims to find new technologies with the potential to become the 'next Google'.
I collaborated with an early-stage team to help prototype and evaluate a novel sensor technology. As the team's software dev,
I built the data pipeline, from sensor interrogation to cloud upload for machine learning. I co-designed a custom PCB, building our
first portable device with improved features, including a 4x size reduction, modular expandability of up to 8x,
2.5x higher sensor resolution, and a portable design with an intuitive user interface.
Through my work with X, I filed two
2 Patent Applications as primary author,
which are now in the public domain. The pending patents describe a gas sensing system consisting of multiple gas sensor modules, each
specific to a set of target analytes. The system can select any subset of the modules to create varied combinations of gasses to
generate broader training data for a machine-learned model. These gas sensors can be pre-sensitised to specific targets with
the addition of a camera module.
We (the sensor team) partnered with Google's Farming Robotics project, Mineral. I
built the hardware and software integration to mount our sensors to the physical robot and interface with Mineral's
ROS robotics system. The data was published to the ROS network in real-time, with the combined sensor readings and GPS
information enabling geo-located insights about the plant health and yield.
Dyson
Three Months Full-Time
@ Dyson HQ, Malmesbury
Concept design
Prototyping
CAD & CAE
DFM & DFA
Highlights:
- Intro to Dyson's rigorous engineering process
- Ownership of feature on an unreleased product
- Offered return role at Dyson upon Graduation.
With thanks to SH et al.
Dyson
Intern @ Dyson [New Product Development]
How can we rethink physical interactions on a product familiar for 20+ years? A paid internship in Dyson's
New Product Development team. I
addressed a design challenge related to an unreleased (confidential) product. As an intern, I was given ownership of part design from
concept generation to design for manufacture. Presenting four solutions across various engineering risk levels, I reimagined
the user interaction challenges. This project sharpened my digital and physical prototyping abilities but inspired me to seek out
faster, more entrepreneurial working environments.
Brompton
Three Months Full-Time
@ Brompton HQ, London
Mechanical
CAD & CAE
Fabrication
AM
Highlights:
- Collaborated as part of an all-intern team
- Redesigned iconic Brompton product
- Fabricated 3 prototype bikes in just 3 months
With thanks to WCS & WBA.
Brompton
Intern @ Brompton Bicycle
How can we reimagine the iconic Brompton Bicycle? A paid internship at Brompton. I
joined a live project team challenged to develop a new Brompton product. As a group of three interns, we began with a blank sheet of
paper and concluded with three fully ridable (and foldable) prototype bikes in just three months. This project kick-started my
computational design and hands-on workshop skillset.
The CEO was highly complimentary of our work, and the project was approved for commercialisation (currently confidential). I
am told the product is still in development and will be released 'soon'. When released, it will be Brompton's
first new core product line in 40+ years.
Imperial
Four Years Full-Time
@ Dyson School of Design Engineering
Design theory
Users
Engineering fundamentals
Innovation
Enterprise
Highlights:
- Awarded Head of School Achievement Prize '20
- Dean’s List for Academic Excellence '18/'19/'20
- IROS 2022 Best Application Paper
With thanks to WB et al.
Imperial College
Design Engineering Master's Degree
How can a degree best teach engineering with design? The
Dyson School is the newest engineering department at Imperial College.
Design Engineering is a highly creative discipline at the intersection of hardware and software. The degree covers the
fundamentals, with an emphasis on product development, technical innovation, user-centred design, and enterprise. 'T-shaped' skillsets
are encouraged; I specialised in physical computing, robotics, interaction, and technical prototyping. Through DesEng, I found a love
for building new things to solve hard problems.
I demonstrated academic excellence over the 4-years, achieving the highest overall degree result of the 2020 class and placing
on the Dean’s List for Academic Excellence in '18/'19/'20 (top 10% of year). My robotics Group project team were awarded an
international robotics prize at IROS 2022 for the Best Application Paper, for our work on a
medical percussion device. During the degree, I interned at Google X, Dyson, and Brompton. I
particularly enjoyed teaching RPi/Arduino after being offered a paid Teaching Assistant role through top-of-class results in
Physical Computing.
OnionBot
One Year Part-Time
@ Imperial College London
Interaction
Algorithms
Computer vision
Research
Python
Front-end
Highlights:
With thanks to DB.
OnionBot
[Open Source] Master's Thesis
Can we augment routine cooking tasks using machine vision and robotics? I designed my Master's project to further my
understanding of ML at a prototype level. OnionBot is a robotic kitchen assistant designed to automate routine pan-cooking tasks. Born
from a desire for a robot that can soften onions while I prepare other ingredients, the prototype showcases the ability to
cook a complete pasta & sauce recipe. Inspired by a great video by the
Experiments by Google team, I shot a film explaining OnionBot. It gained almost 10K views
on YouTube, nearly 25% of the views of the Google video!
Industrial automation technology could also augment home cooking by
reducing errors and supporting decision-making. However, designing automation tech for the home is a unique challenge,
requiring versatile tools instead of specialised machines. While robot arms could replicate human-kitchen interactions, they are too
big and costly for home use. Cameras could act as multi-purpose sensors, but no suitable cooking image datasets are available. With
OnionBot, I combined automation and machine vision techniques into a simple countertop robot. Watch the film on
YouTube
I chose to tackle pan-cooking tasks first. A Raspberry Pi camera and thermal camera are mounted above the stove to monitor
cooking progress. A Coral TPU accelerates classification. A servo motor adjusts
the power setting of the induction stove. The project aims to provide automation without adding excessive complexity;
instead of replacing the chef, OnionBot augments the chef with multitasking superpowers. The human provides the 'actuation', enhanced
by a touchscreen interface 'sous-chef' that offers instructions, reminders, and alerts. OnionBot watches the pan so that the chef can
concentrate on culinary creativity. This human-centred approach is a novel concept in cooking robotics research. Read the
Thesis on Arxiv.
Food image classifiers show poor results in real-world scenarios due to the complexity and variability of food images. OnionBot
introduces two innovations: Firstly, the fixed camera view above the stove provides a consistent environment for capturing
images. Secondly, instead of pursuing a general classification approach, OnionBot adopts a milestone-based method where only
key cooking events, 'milestones', are labelled for each recipe. This simplifies the perception challenge by significantly
reducing the classification scope (from 1000s to 10s). I created a labelling interface to easily build labelled cooking image
datasets by manually selecting each milestone while cooking. I used Google AutoML for streamlined model training, enabling new recipe
models to be trained with just a few clicks. Watch the film on YouTube
The prototype highlights the potential for automation in home cooking but requires large training datasets to advance further. A
fleet of OnionBot devices could crowd-source labelled pan-cooking image data. The fleet-generated dataset, including rich metadata,
could drive new research into cooking with AI. I open-sourced OnionBot to encourage further research; Texas-based Hill Yu
reached out to me; he has built an OnionBot prototype (pictured above) called
Kitchen Automatique and fundraised $40K to commercialise the idea. Wishing
Kitchen Automatique the best of luck!
Percussion
Three Months Part-Time
@ MORPH Robotics Lab Imperial College London
Mechanical design
AM
Simulation
Research
Highlights:
With thanks to PZQ/OT/YT/TN.
Medical Percussion
Robotics Research Group Project
Can we replicate an ancient medical diagnosis tool with modern technology? What is Percussion? Medical percussion involves
tapping the chest, back, and abdomen to assess the condition of underlying tissues based on the resulting acoustic response.
Despite its frequent use in medical diagnosis, percussion dynamics are not fully understood. Experienced practitioners modify
the percussion force and impulse by adjusting the stiffness in their elbow and wrist joints, but the correlation between these
adjustments and the acoustic response remains underexplored. This project explored how robotics and ML could help standardise medical
percussion examinations.
Our Robotics Lab Group introduced a novel robotic percussion device designed to imitate the human percussion technique through
a two-degree-of-freedom linkage mechanism with adjustable joint stiffness. The force profile of a medical student performing
percussion was captured and used to inform the simulation of a mathematical model of the mechanism in MATLAB (above). This allowed for
identifying the optimal parameters to build a hardware prototype. The device was evaluated on a silicone phantom tissue model,
demonstrating a force profile comparable to that of a human performer, with reduced variability between successive percussion
actions.
I contributed to the development and analysis of the initial robotic device. Teammate Oliver Thompson outlines the device in
the presentation below. Oli's presentation was awarded Best Presentation Overall at IROS RoPat20 Robot-Assisted Training For Primary
Care Workshop.
My colleagues continued researching the topic after our graduation. In their first experiment, the device used
spectro-temporal analysis with 1-D Continuous Wavelet Transform (CWT) to identify hard nodules resembling lipomas in silicone phantom
tissue. In their second experiment, Gaussian Mixture Modelling (GMM) and Neural Network (NN) predictive models were used to classify
composite phantom tissue of varying density and thickness. The proposed device and methods achieved up to 97.5% accuracy in the
classification of phantoms, indicating the potential for robotic solutions to
standardise and improve the accuracy of percussion diagnostic procedures. This paper was accepted for publication in
IEEE RA-L.
Pilar Zhang Qiu presented the paper at IROS2022 in Kyoto, Japan. We were thrilled to be awarded the
Best Application Paper Award. This was all
thanks to my wonderful colleagues Pilar Zhang Qiu, Jacob Tan, Oliver Thompson, and our supervisor, Prof Thrishantha
Nanayakkara.
WTHR
Evenings & Weekends
@ Home
Prompt engineering
Python
APIs
Front-End
Highlights:
With thanks to MR.
WTHR forecast
Passion Project
Can we communicate the weather in a more succinct, personable, or useless way using LLMs? I've been wondering why weather
forecasts overwhelm us with so much information, yet I seem to remember so little. Mimi and
I set out to summarise the day's weather in 1 memorable sentence, displayed on an eink screen. Here's what we learnt:
-
We started by hand-crafting the sentence structures, before LLMs took off. GPT3 does a better job (when it isn't making things
up).
-
Through many iterations, we developed good prompts that give accurate results. Data wrangling was the hardest (most important)
part.
-
LLMs can add a lot of character to the forecast with 'style transfer'. We collaborated with ChatGPT to generate 200+ styles
ranging from thought-provoking to useless.
- Formatting for readability on an eink display is hard, we're working on it!
More details on GitHub
Dome
Evenings & Weekends
@ Home
CAD
Fabrication
Raspberry Pi
3D printing
Highlights:
- 60 polycarbonate panels in 3 different sizes
- 35 glazing hubs 3D printed in clear PETG
- 95 wooden struts in 3 different lengths
- There is a reason people build in rectangles!
With thanks to ZK et al.
Geodesic Dome
Passion Project
Can we become self-sufficient by growing vegetables in a (futuristic) greenhouse? After the success of
FarmBot, we built a geodesic dome greenhouse for propagation and overwintering vegetable plants. Built using a
Hubs geodesic frame, kit with a custom polycarbonate glazing + 3D printed solution. Design
spec: Icosahedron, Frequency 2, Subdivision class I, 3/4 Sphere (with flat base).
3D model
Inky
Evenings & Weekends
@ Home
Raspberry Pi
Python
APIs
Highlights:
- View your digital album art in a beautiful frame!
- 7-colour low energy ePaper display
- Check the project out on GitHub
With thanks to TC.
Inky for Spotify
Passion Project
Can we bring back the magic of album art in the digital music era? Use a Raspberry Pi to display album art for your current
Spotify listens on a 7-colour ePaper display. Designed for use with an Inky Impression 7 colour ePaper display. The ePaper display
transitions bring the album art to life and doesn't require power to maintain the image! Check it out on
GitHub
FarmBot
Evenings & Weekends
@ Home
Fabrication
Horticulture
Highlights:
- Automated sowing!
- Automated watering!
- Automated weeding!
With thanks to ZK et al.
FarmBot
Passion Project
Can we become self-sufficient by automating vegetable growing with robotics? FarmBot is an
open-source, automated farming robot for growing food using precision agriculture techniques developed by a team in California.
We built the raised bed, assembled the kit, and programme the growing schedule. Changeable toolheads on a CNC cartesian gantry system
allow FarmBot to sow seeds, water plants and destroy weeds automatically.
This was a lockdown project motivated by the desire to be more self-sufficient by growing our own food. While FarmBot probably
won't save money or time, net, over its lifetime, it did get us into vegetable growing! We had a great crop in the first year, but
90% of the credit goes to the humans looking after FarmBot.
Campervan
Six Months Full-Time
@ Home
CAD
Fabrication
3D printing
Electrical
Plumbing
Gas
Highlights:
- Personal project with friends during our year off
- Unique custom-designed bed mechanism
- All DIY: wood/metalwork, electrics and plumbing
With thanks to CW/HB/DC et al.
Campervan conversion
Passion Project
Can we rethink the typical van conversion and design a layout that is comfortable for 4 guests? Inspired by a love of the
outdoors, I embarked on a campervan conversion project with friends during our year off (to help get out more). We took an unusual
approach to typical van conversions, opting for an aluminium extrusion frame wrapped in lightweight plywood. Sleeping four
comfortably meant thinking outside the box. We had three design goals:
- Four berths and four seats, comfortably.
- Switching from day to night mode should take seconds, not minutes.
- We won't get it 100% right first time, so everything should be removable
We created a first-of-its-kind (as far as we know) bed sliding mechanism to allow us to include both
four berths and four seats, comfortably. The top bed slides over the kitchen area when 'night mode' is deployed. The end result
is extremely comfortable, but comes with a significant increase in complexity. Check out the assembly timelapse and demonstration in
the video below!
For power, I built a fully off-grid solar system, so that the van never needs to be plugged in. I learnt how to spec solar panels,
batteries, controllers etc. through online tutorials and constructed the system myself (see timelapse above). Specs:
- 1200W Inverter for 240V mains power
- 100Ah 12V Lithium battery
- 440W Domestic Solar Panel
- 30A charger from vehicle alternator
- Bluetooth connectivity to phone application
- LED lights, 12V phone chargers, fridge, water pump etc.
Building a campervan was extremely fulflling, but far more of a time investment than I could ever have imagined.
Proceed with caution!