Negli ultimi anni la robotica sta finalmente uscendo dalle fabbriche per popolare le città in cui viviamo. Auto a guida autonoma, droni e robot per la consegna di cibo, quadrupedi per la sorveglianza delle strade: questi sono solo alcuni esempi di ciò che si può trovare già oggi in molti quartieri nel mondo. La rivoluzione generata dal deep learning a partire dal 2012 è soltanto uno degli elementi di questa diffusione, che si fonda anche su complesse dinamiche di mercato e decenni di ricerca precedente nell'ambito dei sistemi robotici, dal punto di vista sia software che hardware. A che punto siamo arrivati? Quali sono le sfide che i ricercatori e le aziende devono affrontare oggi in questo settore? Quali sono i meccanismi di mercato che guidano lo sviluppo di questi sistemi? In questo talk risponderemo a queste domande, in modo da fornire una panoramica completa sullo stato dell'arte nella robotica mobile urbana.
Autonomous Vehicles: the Intersection of Robotics and Artificial IntelligenceWiley Jones
Autonomous Vehicle Webinar. Crash course in AVs: high-level overview, technology deep-dives, and trends. Follow me on Twitter at https://twitter.com/wileycwj.
Link to YouTube Video: https://www.youtube.com/watch?v=CruCp6vqPQs
Google Slides: https://docs.google.com/presentation/d/1-ZWAXEH-5Xu7_zts-rGhNwan14VH841llZwrHGT_9dQ/edit?usp=sharing
Presentazione al Meetup di Marzo del Machine Learning / Data Science Meetup di Roma: https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/248063386/
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/qualcomm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-talluri
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raj Talluri, Senior Vice President of Product Management at Qualcomm Technologies, presents the "Is Vision the New Wireless?" tutorial at the May 2016 Embedded Vision Summit.
Over the past 20 years, digital wireless communications has become an essential technology for many industries, and a primary driver for the electronics industry. Today, computer vision is showing signs of following a similar trajectory. Once used only in low-volume applications such as manufacturing inspection, vision is now becoming an essential technology for a wide range of mass-market devices, from cars to drones to mobile phones. In this presentation, Talluri examines the motivations for incorporating vision into diverse products, presents case studies that illuminate the current state of vision technology in high-volume products, and explores critical challenges to ubiquitous deployment of visual intelligence.
Fullstop.ai is a level 2 autonomous hardware and software solution by Synergy Robotics and UMA Robotics, with Nvidia Hardware, with Provable ML, using a provable algorithm for a leader follower algorithm based , autonomous navigation system, incorporating Nvidia Jetson SDK, Drive OS, and smart Cities SDK. Similar to Comma.ai
Autonomous Vehicles: the Intersection of Robotics and Artificial IntelligenceWiley Jones
Autonomous Vehicle Webinar. Crash course in AVs: high-level overview, technology deep-dives, and trends. Follow me on Twitter at https://twitter.com/wileycwj.
Link to YouTube Video: https://www.youtube.com/watch?v=CruCp6vqPQs
Google Slides: https://docs.google.com/presentation/d/1-ZWAXEH-5Xu7_zts-rGhNwan14VH841llZwrHGT_9dQ/edit?usp=sharing
Presentazione al Meetup di Marzo del Machine Learning / Data Science Meetup di Roma: https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/248063386/
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/qualcomm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-talluri
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raj Talluri, Senior Vice President of Product Management at Qualcomm Technologies, presents the "Is Vision the New Wireless?" tutorial at the May 2016 Embedded Vision Summit.
Over the past 20 years, digital wireless communications has become an essential technology for many industries, and a primary driver for the electronics industry. Today, computer vision is showing signs of following a similar trajectory. Once used only in low-volume applications such as manufacturing inspection, vision is now becoming an essential technology for a wide range of mass-market devices, from cars to drones to mobile phones. In this presentation, Talluri examines the motivations for incorporating vision into diverse products, presents case studies that illuminate the current state of vision technology in high-volume products, and explores critical challenges to ubiquitous deployment of visual intelligence.
Fullstop.ai is a level 2 autonomous hardware and software solution by Synergy Robotics and UMA Robotics, with Nvidia Hardware, with Provable ML, using a provable algorithm for a leader follower algorithm based , autonomous navigation system, incorporating Nvidia Jetson SDK, Drive OS, and smart Cities SDK. Similar to Comma.ai
The field of DL has matured a lot in the last decade and changed a lot in the last few years. New architectures scaled to be larger/deeper, take advantage of a large number of datasets and parallel computing power.
Supervised DL methods, namely, CNNs and RNNs, are the natural choice for researchers in the automotive domain.
Important aspects such as compute power requirements, model transparency, and interpretability, model compliance with vehicle safety standards, all of which are expected to appreciably impact the adoption rate of DL in the automotive industry.
Problems in Autonomous Driving System of Smart Cities in IoTijtsrd
This paper focuses on the problems and challenges during self driving. In the modern era, technologies are getting advanced day by day. The field of smart city has introduced a new technology called ""Autonomous Driving"". Autonomous driving can be defined as Self Driving, Automated Vehicle. Google has started working on this type of system since 2010 and still in the phase of making changes in this technology to take it to a higher level. Any technology can reach up to an advanced level but it cannot provide a full fledged result. This paper facilitates the researchers to understand the problems, challenges and issues related to this technology. Shweta S. Darekar | Dr. Anandhi Giri ""Problems in Autonomous Driving System of Smart Cities in IoT"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30079.pdf
Paper Url : https://www.ijtsrd.com/computer-science/other/30079/problems-in-autonomous-driving-system-of-smart-cities-in-iot/shweta-s-darekar
Marek Jersak. Autonomous Drive – From Sensors to MotionIT Arena
Marek Jersak, Senior Director, Autonomous Drive Practice at Luxoft Automotive
Autonomous Drive – From Sensors to Motion
Dr. Marek Jersak received his Diploma in Electrical Engineering from Aachen University of Technology, Germany in 1997. From 1997 to 1999 he worked as a compiler design engineer for Conexant Systems in Newport Beach, California. He returned to school in 1999 and graduated with a PhD in Real-Time Embedded System Design from the Technical University of Braunschweig, Germany in 2004. Together with his university fellow Kai Richter, in 2005 Marek co-founded Symtavision GmbH in Braunschweig, and in 2013 Symtavision Inc in Michigan, serving as Managing Director respectively President for those companies. Symtavision became a globally recognized leader in Timing Analysis tools and architecture consulting for automotive real- time systems with a focus on chassis, active safety, powertrain, body-control and in-vehicle networking. In February 2016, Marek and Kai sold Symtavision to Luxoft. Marek became director of the newly formed ‘Under the Hood’ practice inside Luxoft Automotive. The practice grew to more than 200 engineers in 1.5 years. At the end of 2017, we repositioned the practice to focus fully on various levels of automated driving, from Level-2 / 3 mass-production ADAS software to architectures and algorithms for Level-4 and ultimately Level-5 autonomous driving. Marek is now fully focused on building the teams, customer relationships and engagement models that enable a seamless, scalable and agile solutions offering from sensors to actuators, spanning co-development with our customers of system and software architectures, algorithms, automotive-grade software, integration, and testing.
Autonomous Vehicles: Technologies, Economics, and OpportunitiesJeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of autonomous vehicles are improving rapidly. LIDAR, other sensors, ICs, and wireless are experiencing rapid improvements that are enabling the overall cost of AVs to fall. For example, the latency of wireless systems is improving rapidly thus enabling vehicles to be controlled with wireless systems. This is also creating many new opportunities in the vehicle industry in the Internet of Things, data analytics, and logistics. The slides include a detailed discussion of AVs in Singapore, a likely early adopter.
Robotics - Mainstream or Marginal for Process Industries?Yokogawa1
Whereas robotics have been crucial to factory and warehouse automation for decades, the technology in process industry applications continues to be relatively new and emerging. Savvy operators are realizing the value of robotics in enhancing process safety and efficiency in daily operation and maintenance activities. Robots are performing operator runs in hazardous areas and drones have been deployed on inspections routes in difficult-to-access locations across process complexes. Yet, process operators still face many challenges when it comes to widely adopting robots. How can users integrate robotics into existing operations, control systems and standard operating procedures? Can they use the data collected from robots and combine it with artificial intelligence to provide actionable knowledge? Where do robotics reside on the digital transformation roadmap that ultimately leads to autonomous operations? In this session, participants will learn about Yokogawa's robotics vision, which steps beyond Industry 4.0 and digital transformation to achieve industrial autonomy.
What are the current scenarios of traffic signal monitoring system?JosephCraven4
A Traffic signal monitoring system assumes a fundamental part in the administration of metropolitan traffic, mostly in the profoundly populated metropolitan locales.
Get in Touch:
Location: Manufactured By STC, Inc. 1201 W. Randolph St, McLeansboro, IL 62859
Phone: Richard D’Alessandro: (214) 607–0100
Fax: (214) 607–0105
Email: info@emtracsystems.com
Web: www.emtracsystems.com/
The field of DL has matured a lot in the last decade and changed a lot in the last few years. New architectures scaled to be larger/deeper, take advantage of a large number of datasets and parallel computing power.
Supervised DL methods, namely, CNNs and RNNs, are the natural choice for researchers in the automotive domain.
Important aspects such as compute power requirements, model transparency, and interpretability, model compliance with vehicle safety standards, all of which are expected to appreciably impact the adoption rate of DL in the automotive industry.
Problems in Autonomous Driving System of Smart Cities in IoTijtsrd
This paper focuses on the problems and challenges during self driving. In the modern era, technologies are getting advanced day by day. The field of smart city has introduced a new technology called ""Autonomous Driving"". Autonomous driving can be defined as Self Driving, Automated Vehicle. Google has started working on this type of system since 2010 and still in the phase of making changes in this technology to take it to a higher level. Any technology can reach up to an advanced level but it cannot provide a full fledged result. This paper facilitates the researchers to understand the problems, challenges and issues related to this technology. Shweta S. Darekar | Dr. Anandhi Giri ""Problems in Autonomous Driving System of Smart Cities in IoT"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30079.pdf
Paper Url : https://www.ijtsrd.com/computer-science/other/30079/problems-in-autonomous-driving-system-of-smart-cities-in-iot/shweta-s-darekar
Marek Jersak. Autonomous Drive – From Sensors to MotionIT Arena
Marek Jersak, Senior Director, Autonomous Drive Practice at Luxoft Automotive
Autonomous Drive – From Sensors to Motion
Dr. Marek Jersak received his Diploma in Electrical Engineering from Aachen University of Technology, Germany in 1997. From 1997 to 1999 he worked as a compiler design engineer for Conexant Systems in Newport Beach, California. He returned to school in 1999 and graduated with a PhD in Real-Time Embedded System Design from the Technical University of Braunschweig, Germany in 2004. Together with his university fellow Kai Richter, in 2005 Marek co-founded Symtavision GmbH in Braunschweig, and in 2013 Symtavision Inc in Michigan, serving as Managing Director respectively President for those companies. Symtavision became a globally recognized leader in Timing Analysis tools and architecture consulting for automotive real- time systems with a focus on chassis, active safety, powertrain, body-control and in-vehicle networking. In February 2016, Marek and Kai sold Symtavision to Luxoft. Marek became director of the newly formed ‘Under the Hood’ practice inside Luxoft Automotive. The practice grew to more than 200 engineers in 1.5 years. At the end of 2017, we repositioned the practice to focus fully on various levels of automated driving, from Level-2 / 3 mass-production ADAS software to architectures and algorithms for Level-4 and ultimately Level-5 autonomous driving. Marek is now fully focused on building the teams, customer relationships and engagement models that enable a seamless, scalable and agile solutions offering from sensors to actuators, spanning co-development with our customers of system and software architectures, algorithms, automotive-grade software, integration, and testing.
Autonomous Vehicles: Technologies, Economics, and OpportunitiesJeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of autonomous vehicles are improving rapidly. LIDAR, other sensors, ICs, and wireless are experiencing rapid improvements that are enabling the overall cost of AVs to fall. For example, the latency of wireless systems is improving rapidly thus enabling vehicles to be controlled with wireless systems. This is also creating many new opportunities in the vehicle industry in the Internet of Things, data analytics, and logistics. The slides include a detailed discussion of AVs in Singapore, a likely early adopter.
Robotics - Mainstream or Marginal for Process Industries?Yokogawa1
Whereas robotics have been crucial to factory and warehouse automation for decades, the technology in process industry applications continues to be relatively new and emerging. Savvy operators are realizing the value of robotics in enhancing process safety and efficiency in daily operation and maintenance activities. Robots are performing operator runs in hazardous areas and drones have been deployed on inspections routes in difficult-to-access locations across process complexes. Yet, process operators still face many challenges when it comes to widely adopting robots. How can users integrate robotics into existing operations, control systems and standard operating procedures? Can they use the data collected from robots and combine it with artificial intelligence to provide actionable knowledge? Where do robotics reside on the digital transformation roadmap that ultimately leads to autonomous operations? In this session, participants will learn about Yokogawa's robotics vision, which steps beyond Industry 4.0 and digital transformation to achieve industrial autonomy.
What are the current scenarios of traffic signal monitoring system?JosephCraven4
A Traffic signal monitoring system assumes a fundamental part in the administration of metropolitan traffic, mostly in the profoundly populated metropolitan locales.
Get in Touch:
Location: Manufactured By STC, Inc. 1201 W. Randolph St, McLeansboro, IL 62859
Phone: Richard D’Alessandro: (214) 607–0100
Fax: (214) 607–0105
Email: info@emtracsystems.com
Web: www.emtracsystems.com/
"Attention Is All You Need" Grazie a queste semplici parole, nel 2017 il Deep Learning ha subito un profondo cambiamento. I Transformers, inizialmente introdotti nel campo del Natural Language Processing, si sono recentemente dimostrati estremamente efficaci anche al di fuori di questo settore, ottenendo un enorme - e forse inaspettato - successo nel campo della Computer Vision. I Vision Transformers e moltissime delle sue varianti stanno ridefinendo oggi lo stato dell'arte su molti task di visione artificiale, dalla classificazione di immagini fino ai sistemi di visione per la guida autonoma. Ma cosa sono i Transformers? In che cosa consiste il meccanismo della self-attention che è alla base del loro funzionamento? Quali sono i suoi limiti? Saranno in grado di rimpiazzare le famose reti convoluzionali che hanno, a loro tempo, rivoluzionato la Computer Vision? In questo talk cercheremo di rispondere a tutte queste domande, offrendo un'ampia panoramica sulle idee fondanti, sulle architetture Transformer più utilizzate, e sulle applicazioni più promettenti.
Thanks to Machine Learning (ML), a large amount of data has been put to good use in the recent years, detecting patterns, extracting insights and providing valuable predictions to decision makers.
However, having actionable knowledge is only part of the picture. Be it a robot or a human, once the data is in, a course of action, possibly the best, has to be found, while satisfying several requirements.
For example, deciding which stocks to buy and when, how to schedule deliveries, which widget to produce and in which machinery to invest, are all decision problems.
Operations Research (OR) tries to reliably answer those questions by explicitly modelling decisions and finding the optimal ones for the chosen goals.
Given the tremendous impact decision optimization can yield for a business, OR is one of the best way to exploit and valorize ML models!
In this talk I will present Mathematical Programming, a versatile decision modelling method, and its application to an example Power Plant scheduling problem, solved via open source solvers and Python libraries.
While smaller problems can be solved in reasonable time with a generic solver on a single machine, the same technology is routinely scaled in real-world applications to larger and more complex problems via
generic “decomposition methods”.
I will then present an example of decomposition for the Power Plant problem.
Molti esperimenti nell'ambito delle scienze naturali sono basati sul conteggio di strutture biologiche di interesse, e.g. il numero di cellule che interagiscono con specifici reagenti in diverse condizioni sperimentali. Malgrado il riconoscimento di queste strutture non necessiti tipicamente di particolare expertise, un'ispezione manuale dei campioni da parte di operatori umani è molto onerosa in termini di tempo e personale impiegato. Inoltre, questo processo è soggetto ad errori di varia natura dovuti all'affaticamento degli operatori e alla loro interpretazione soggettiva di alcuni casi limite. L'automatizzazione di questo processo è quindi fondamentale per accelerare gli sviluppi nel settore e permettere confronti più equi tra esperimenti.
In questo meetup approfondiremo cell-ResUnet (c-ResUnet), un approccio Deep Learning che permette di riconoscere e contare cellule neuronali in immagini di microscopia in fluorescenza,
sfruttando una architettura e delle strategie di addestramento pensati specificatamente per questa applicazione.
L’identificazione di anomalie è una tematica sempre più popolare che viene affrontata su più fronti. In generale, l’anomalia rappresenta un’entità, un evento o una caratteristica che non risulta conforme allo standard di normalità. Le anomalie sono un ostacolo, a volte anche pericoloso come per esempio nella sicurezza informatica, in cui l’intrusione di persone non fidate all’interno di sistemi informatici può diventare critico per un’azienda o un’istituzione; in industrie invece, le anomalie possono danneggiare la qualità dei prodotti, causando pesanti perdite in termini economici. Per questo motivo vengono ideate numerose tecniche che permettono di riconoscere le anomalie e ridurre i pericoli, i danni da esse causate o semplicemente per monitorare la qualità e gestire la manutenzione.
In un contesto di immagini, il riconoscimento di anomalie è un problema di Computer Vision. Esistono metodi di ricostruzione come gli Autoencoder o metodi generativi come le GAN che si occupano di risolvere tale problema. Tra i modelli che si basano sulle GAN, chiamati GAN-based, si distingue il modello Ganomaly: esso permette di rilevare se un’immagine sia anomala.
Sulla base di quest’ultimo, nascono Patch-Ganomaly, con cui si vuole migliorare il comportamento di Ganomaly, andando a localizzare la regione anomala di un’immagine, in termini di pixel, e migliorarne efficacia ed efficienza.
Mediante l’utilizzo di transfer learning basato sulla rete VGG16 è possibile ottenere un modello più preciso, TL-Ganomaly. Esso localizza la regione anomala in maniera precisa, in termini di pixel riconosciuti correttamente anomali.
In fase di post-processing inoltre è possibile dare un ulteriore apporto con il modello Conv-Processing, il quale apprende quale kernel convoluzionale riesca a migliorare la segmentazione delle anomalie in fase di post-processing.
Towards Quantum Machine Learning Hands-on
Machine Learning (ML) gained a lot of momentum in the last ten years, mostly thanks to the advancements in non-linear patterns discovery, and more specifically, in Deep Learning (DL). But those who think that DL is going to address all possible problems might be terribly wrong. DL and ML tasks, in general, are categorized as Non-Polynomial problems, which means that the number of possible solutions for a given problem can grow exponentially, making it intractable using the classical algorithmic approach. Here, Quantum Computing (QC) techniques have the potential to address these issues and help ML methods to solve problems faster and sometimes better than the classical counterpart. The conjunction of these two disciplines resulted in a new exciting research direction to explore: Quantum Machine Learning (QML).
towards Quantum Machine Learning
Machine Learning (ML) gained a lot of momentum in the last ten years, mostly thanks to the advancements in non-linear patterns discovery, and more specifically, in Deep Learning (DL). But those who think that DL is going to address all possible problems might be terribly wrong. DL and ML tasks, in general, are categorized as Non-Polynomial problems, which means that the number of possible solutions for a given problem can grow exponentially, making it intractable using the classical algorithmic approach. Here, Quantum Computing (QC) techniques have the potential to address these issues and help ML methods to solve problems faster and sometimes better than the classical counterpart. The conjunction of these two disciplines resulted in a new exciting research direction to explore: Quantum Machine Learning (QML).
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
2. Automata
Francesco, Michelangelo, Marco
Former colleagues at YAPE, now good friends and
co-founders
What do we do?
● Tech advisory to startups around robotics and
autonomous driving
● Teaching and promotion of STEM through
Masters, Webinars, Meetup and Masterclasses
● Mentoring
3. About Me
La Sapienza / UC Berkeley
Yape
INSEAD
Francesco Ricciuti
ricciuti.fr@gmail.com
https://www.linkedin.com/in/f-ricciuti/
Born and raised in Rome
BSc in Engineering Sciences @ Tor Vergata
MSc in Control and Automation Engineer @ La Sapienza
Visiting Student Researcher @ UC Berkeley
Robotics Software Engineer @ Yape
MBA Candidate @ INSEAD
4. this was in 2015!
Source: Wired
Source: Fortune
Autonomy is hard!
5. Where we are today
Starship Robotics
Founded in 2014, 3M autonomous deliveries to date. For comparison, it takes Deliveroo less than 4 days to do 3M
deliveries
Cruise
Raised $15.1B to date. Recently started fully autonomous service, covering only 70% of San Francisco
6. If it’s so damn hard, why are people still investing money in it?
Source: TechCrunch
Source: Cruise
7. The market need: food delivery
Food delivery companies are BLEEDING MONEY
Source: Just Eat annual report 2021
…but the market opportunity is huge!
€ 36B:
Food Delivery revenues,
Europe
€ 1,5B:
Food Delivery revenues,
Italy
8. The market need: urban mobility
Our cities are built around cars: 55% of the space dedicated to transportation is used by cars
The cycle of automobile dependency
Source: “Urban Space Distribution and Sustainable Transport”
5.3 deaths per
100k inhabitants:
Road fatalities, Italy
Driving is dangerous
9. The regulatory framework: autonomous robotics
countries with ongoing
commercial operations
countries with ongoing
experimental trials
Sources: Reuters, El Pais, Mirror, Business Korea, CTV News
11. Two different approaches
Remote first Autonomous first
Solve logistics and win empty market,
then think of unit economics
Cutting-edge technology, very cheap
deliveries but longer go-to-market times
(and costs…)
12. Whoami
Skills: Roboticists full-time,
Tech advisor and proud
co-founding member of Automata.
Name: Michelangelo Setaro
Contact: LinkedIn
Experience: Senior AD Software
Engineer at Stellantis and
former Lead Robotics Software
Engineer at Yape
13. What does autonomy means?
Let’s borrow some notions from automotive
We are here
14. High Level Design
Perception
Business Logic
Domain Specific
Communication
Diangostics
Watchdogs
Motion Planning
HAL
HMI
Actuation
Sensing
Hardware
Portrait of Autonomy
Cloud
Middleware
Software
Safety
Network
Remote
Supervision &
Teleoperations
Mapping &
Localization
15. System Design - Sensing Hardware
Nuro
● Lidar
● Ultrasonic
● Long & short range radar
● 360° Camera
Camello
● Ultrasonic
● Lidar
● Front Camera
Starship
● Ultrasonic
● Radar
● 12 Camera
17. Perception - Panoptic segmentation example
Everybody loves to watch a good video
Panoptic segmentation: DNN allow for pixel
level object information, thus fusing
together instance & semantic segmentation
Instance segmentation: identifies individual
objects within given category
Semantic segmentation: multiple objects of
the same category are considered one entity
18. Mapping and Localization
Autonomy means being aware of current position in space
Requires complex infrastructure and data
storage (1km x 1km map means at least 20GB)
Requires continuous data processing to
create/maintain maps
Higher the desired level of autonomy, more
complex maps become
Don’t trust who says “Tesla doesn’t use maps”
Google Cartographer - Pose Graph Slam
19. Mapping and Localization - Google Cartographer
Everybody loves to watch a good video cont’d
A great example of pose-graph slam, made by
google and Magazino
Works both with 3D and 2D lidar
Has a great cloud implementation that can be
a starting point for a centralized mapping
and localization infrastructure
20. Lacks of standard regulations, thus
difficulty to generalize and scale-up
Why autonomous driving is getting closer but not autonomous robotics?
Robotics
Automated driving is regulated by crystal
clear rules
Automotive
Petabytes of consolidated and constantly
updated SD/HD maps
Thousands of driving environment reference
datasets for training/testing/benchmarking
Each startup must rely on its proprietary
technology and infrastructure
Operating environment for urban robots is not
well defined, thus no dataset is really
general
Robotics is a capital intensive industry
with very demanding technological
constraints, few successful robotics
startup use the same approach of automotive
Main Challenges
Robotics vs Automotive