• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to secondary sidebar
  • About
    • Contact
    • Privacy
    • Terms of use
  • Advertise
    • Advertising
    • Case studies
    • Design
    • Email marketing
    • Features list
    • Lead generation
    • Magazine
    • Press releases
    • Publishing
    • Sponsor an article
    • Webcasting
    • Webinars
    • White papers
    • Writing
  • Subscribe to Newsletter

Robotics & Automation News

Where Innovation Meets Imagination

  • Home
  • News
  • Features
  • Editorial Sections A-Z
    • Agriculture
    • Aircraft
    • Artificial Intelligence
    • Automation
    • Autonomous Vehicles
    • Business
    • Computing
    • Construction
    • Culture
    • Design
    • Drones
    • Economy
    • Energy
    • Engineering
    • Environment
    • Health
    • Humanoids
    • Industrial robots
    • Industry
    • Infrastructure
    • Investments
    • Logistics
    • Manufacturing
    • Marine
    • Material handling
    • Materials
    • Mining
    • Promoted
    • Research
    • Robotics
    • Science
    • Sensors
    • Service robots
    • Software
    • Space
    • Technology
    • Transportation
    • Warehouse robots
    • Wearables
  • Press releases
  • Events
starship tech robot

Insight: How neural networks are powering autonomous delivery

December 4, 2018 by Sam Francis

By Tanel Pärnamaa, deep learning engineer at Starship Technologies

Artificial neural networks are one of the main tools used in machine learning to convert unstructured, low-level data into higher level information. As the ‘neural’ part suggests, they are brain-inspired systems intended to replicate the way that humans learn.

At Starship, we are building a fleet of autonomous delivery robots using trainable units, including mostly neural networks, where code is written by the model itself. The robots are recording and processing large sets of data in order to recognise surroundings and how to react in real time as each situation requires.

To begin with, the robots start by gaining a sense of the world through radars, a multitude of cameras and ultrasonics. However, this poses challenges because most of this knowledge gained is low-level and non-semantic. 

For example, a robot may be able to sense that an object (pedestrian, bicycle, animal and so on) is 10 metres away, but without knowing what category the object falls into it’s much harder for the robot to make a decision on the best course of action. It’s machine learning through neural networks that is extremely useful in converting this unstructured low-level data into higher level information.

In the case of robots that drive safely on pavements/sidewalks and need to cross streets, it is critical for there to be an understanding of the surrounding environment in real time. Take the example above of pedestrians or cyclists; it’s not only vital to be aware of their presence, but also what direction they are moving in and how quickly.

To be able to ascertain this sort of information, a central component is an object detection module – a program that inputs images and returns a list of object boxes. But this in itself is not straightforward because an image is a large 3D array made up of a multitude of numbers that represent pixel intensities.

These values change significantly in different environments. For example, if an image is taken at night rather than during the day, when the object’s colour or position changes, or when the object itself is obstructed.

This means that in some cases teaching is a better solution than programming. As mentioned, at Starship we have a set of trainable units, mostly neural networks, where the code is written by the model itself. The program is represented by a set of weights and we can visualise what each specific neuron is trying to detect.

For example, the first layers of our network activate to standard patterns like horizontal and vertical edges. The next block of layers detect more complex textures, while higher layers detect car parts and full objects.

Our engineers present the model examples of what they would like to predict and ask the network to get better at doing so the next time it sees a similar input. By iteratively changing the weights, the optimisation algorithm searches for programs that predict bounding boxes (imaginary boxes around objects that are being checked for collision) more and more accurately.

However, when teaching a machine, big data is merely not enough. The data collected must be rich and varied. For example, only using equally sampled images and then annotating them would display numerous pedestrians and cars for example, but the model would lack examples of bicycles, animals or other objects to reliably detect these categories as well.

At the same time, annotating data takes time and resources. Ideally, it’s best to train and enhance models with less data. This is where architecture engineering comes into play, in terms of encoding prior knowledge into the architecture and optimisation processes to reduce the search space to programs that are more likely in the real world.

This is useful in the case of autonomous delivery because the model needs to know whether a robot is on a pavement/sidewalk or crossing a road. By encoding the relevant global context into the neural network architecture, the model then determines whether to use it or not without having to learn it from scratch each time.

Neural networks empower Starship’s robots to be safe on road crossings by avoiding obstacles like cars, and on sidewalks by understanding all the different directions that humans and other obstacles can choose to go.

To date, the robots have safely travelled over 125,000 miles in more than 20 countries and 100 cities. They are on streets in cities around the world right now on a daily basis, navigating pavements, crossings and pedestrians, without the support of a human pilot or large processing systems, to offer automated deliveries to consumers.

Print Friendly, PDF & Email

Share this:

  • Click to print (Opens in new window) Print
  • Click to share on Facebook (Opens in new window) Facebook
  • Click to share on LinkedIn (Opens in new window) LinkedIn
  • Click to share on Reddit (Opens in new window) Reddit
  • Click to share on X (Opens in new window) X
  • Click to share on Tumblr (Opens in new window) Tumblr
  • Click to share on Pinterest (Opens in new window) Pinterest
  • Click to share on WhatsApp (Opens in new window) WhatsApp
  • Click to share on Telegram (Opens in new window) Telegram
  • Click to share on Pocket (Opens in new window) Pocket

Related stories you might also like…

Filed Under: Features, Logistics Tagged With: autonomous delivery robots, deep learning, neural networks, starship technologies

Primary Sidebar

Search this website

Latest articles

  • Mitsubishi Electric says its robots are ‘bridging skills gaps’ in automation technology
  • Pusan National University scientists develop ‘game-changing method’ to create safer, long-lasting lithium-ion batteries
  • Fraunhofer develops ‘resource-efficient’ measurement system for semiconductor wafer production
  • Tokyo University scientists discover key to ‘stable, high-performance, and long-life’ sodium-ion batteries
  • Swiss Steel develops ‘sustainable and easy-to-machine special steels’ for automotive industry
  • How to Calculate Diminished Value for Your Car After an Accident
  • Gartner predicts one in 20 supply chain managers will manage robots, not humans, by 2030
  • Moldova launches new incubator for robotics, digital agriculture, and foodtech
  • Pudu Robotics launches new industrial sweeper and vacuum
  • Out of thin air: MIT engineers develop device that creates safe drinking water from air

Secondary Sidebar

Copyright © 2025 · News Pro on Genesis Framework · WordPress · Log in

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept”, you consent to the use of ALL the cookies.
Do not sell my personal information.
Cookie SettingsAccept
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
CookieDurationDescription
cookielawinfo-checkbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytics
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Others
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
SAVE & ACCEPT