Robotiq.ai and Stefanini will work together to use robotic and AI solutions to accelerate digital transformation in the financial industry

Robotiq.ai partners with Stefanini

Robotiq.ai, a platform that harnesses the power of Robotic Process Automation (RPA) and AI solutions joins forces with Stefanini, a service and software provider, for data processing and consulting.

Embracing the Future: Stefanini and Robotiq.ai Join Forces to Accelerate Digital Transformation in the Financial Industry

Robotiq’s latest palletising solution, the PE20, is designed for the new UR20 collaborative robot (cobot) from Universal Robots. The UR20 is the largest 6-axis cobot arm in the range. The solution is now available to order in the UK from leading automation distributor RARUK Automation. The new Universal Robots model offers the greatest reach and payload capabilities of the entire UR cobot range. One of the key features of the new UR20 is its re-designed joints that allow increased speed and torque. This redesign cuts cycle times by up to 65%, as well as enabling the robot to handle heavier loads. Where palletising is concerned, the new UR model’s reach is ideal for standard EURO pallets. As a Robotiq Expert Partner, RARUK Automation already offers a range of Robotiq application solutions to help manufacturing businesses automate their processes across the UK. The off-the-shelf application solutions include Machine Tending, Screwdriving and the AX and PE Series Palletising Solutions. Robotiq’s new solution has been developed to utilise the UR20’s key features for enhanced palletising. The PE20 can handle payloads up to 20 kg (44 lb) with end of arm tooling, unlocking new possibilities in terms of palletising. The solution offers speeds reaching 13 picks per minute, enabling manufacturers to cycle various product types quickly. The new Robotiq Palletising Solution is compatible with all standard pallet dimensions. The PE20 reaches stack heights of up to 2.15 m (84 in). Robotiq's software solution ensures accuracy and ease in managing a high number of SKUs. The solution offers even higher productivity and efficiency than previous automated palletising options. When paired with Robotiq’s enhanced PowerPick gripper and Multipick software feature, the PE20 will be able to palletise multiple boxes at once. With 16 possible gripper configurations, the PE20’s flexibility will allow users to manage changing production line needs and easily adapt to future requirements. “Robotiq’s new solution is a fully integrated software and hardware package, which is easy to program and set up.” Says Andrew Mason, Automation Sales Manager for RARUK Automation. “We are on hand to assist our customers with deployment, training and ongoing technical support.” Andrew Mason concludes: “We are pleased to be able to offer the PE20 to our UK customers, as the increased payload and reach capabilities of the new UR20 model will enable businesses to automate their more challenging palletising tasks.” RARUK Automation can now accept orders for the PE20.

Introducing the PE20: The New, Powerful Palletising Solution from Robotiq

The latest documented market research study on Robot Grippers Market published with MarketandResearch.biz discusses the industry size, share, market outlook, segments, regional insights.

Robot Grippers Market is expected to witness Incredible Growth during 2023-2029 | Schunk, Festo, SMC, Robotiq

Key Players in This Report Include: Robotiq, Yaskawa Motoman, Empire Robotics. - Middle East and Africa (South Africa, Saudi Arabia, UAE, Israel, Egypt, etc.) - North America (United States ...

Adaptive Robot Gripper Market 2023 Technology Advancements and Research-Robotiq, Yaskawa Motoman, Empire Robotics,

Designed from the ground up to be robust, adaptable, and easily configurable, Robotiq’s new PowerPick gripper brings together the learnings from hundreds of palletizing applications. It boasts ...

Robotiq PowerPick

In the most recent 2023 publication by Courant Market Research, their report focuses on the extensive research conducted for the Global Robot Gripper Market. The study encompasses an in-depth analysis of the Robot Gripper industry, encompassing its current ...

World Robot Gripper Market Analysis Of Trends, Demand And Competition 2023-2030 – Schunk, Festo, SMC, Robotiq, Zimmer, Destaco, EMI, IAI

Robot Force Sensors Market report provides valuable insights for individuals interested in valuable data, trends,

Robot Force Sensors Market Latest Trends, Demand, Cost Structures and Industry Development 2023-2030 |FANUC, OnRobot, Robotiq

Robot Force Sensors Market Outlook, Opportunity and Demand Analysis Report by 2030 |FANUC, OnRobot, Robotiq

T he Robotiq FT 150 force torque sensor can be used in a lot of applications. From assembly tasks to collaborative uses, the sensor is designed for a range of applications. With the constant quest to bring products with high quality standards to the ...

Robots Get A Grip

Robotiq

robotiq

Time-Sensitive Networking for robotics

There is a recent article in , , stating that: Scientific American What Does It ‘Feel’ Like to Be a Chatbot? "Consciousness, on the other hand, is about states of being—seeing the blue sky, hearing birds chirp, feeling pain, being in love. The difference between the real and the simulated is their respective causal powers. That’s why it doesn’t rain inside a computer simulating a rainstorm. The software is functionally identical to weather yet lacks its causal powers to blow and turn vapor into water drops. Causal power, the ability to make or take a difference to itself, must be built into the system. This is not impossible. A so-called neuromorphic or bionic computer could be as conscious as a human, but that is not the case for the standard von Neumann architecture that is the foundation of all modern computers. Once we have a solid explanation of human consciousness and its neural underpinnings, we can extend such an understanding to intelligent machines in a coherent and scientifically satisfactory manner." What Is Consciousness? Consciousness is described with states of being or subjective experiences, like 'seeing the blue sky, hearing birds chirp, feeling pain, being in love'. However, is consciousness just one subjective experience in one moment, or is consciousness all the possible subjective experiences for an individual? If there are several possible subjective experiences, then could there be different divisions or collections of consciousness? Also, must the subjective experience be these few regular examples of subjective experiences? A computer simulation of rain does not rain, but if an individual imagines what rain is, there is the possibility of having some feelings of what it means to be in the rain, the cold, sound, or others. Does the imagination not count, for the human as a lower version of subjective experience, albeit the event is not happening? Though it is observed that there is attention to consciousness, there is also a range to which humans can be conscious, making consciousness diversified. Of these diversifications, can chatbots not have some? If an individual asks another a personal question and gets an answer, are the question and answer not consciousness? The question is heard just like how birds chirp; the answer is provided subjectively, sometimes with the emotion or trauma of the time—depending on the question. So, questions and answers can also be consciousness, with or without the emotional variables. If the question was written, or something is read, just like seeing the blue sky, is reading not consciousness? It is known that chatbots do not have stomachs, so they cannot hear visceral sounds or know that it is from them. They also don't feel pain as they can be struck and not know. But, if humans know what pain is as a condition, and the memory of what happened to another person can lead to a feeling, or a visceral reaction can be understood when described, does it not show that consciousness can just be the memory of something, aside from the feeling? Describing consciousness as 'seeing the blue sky, hearing birds chirp, feeling pain, being in love' also means what? Knowing what the—blue sky, birds chirping, the feeling of pain, and being in love—are. If knowing those things in an attached manner or subjectively, means consciousness, can consciousness not be explained with the range of what can be known? And if knowing includes balance, movement, and regulation, can knowing not be extended beyond regular descriptions of things in conscious awareness? Consciousness can be described as all that can be known by humans, with a total of 1. Consciousness could have been described here as a subset of what can be known but the mechanisms from which the brain organizes knowing across processes seem similar, so even if consciousness is different say from balance, since they are both known, the blue sky and that an individual is walking and balanced with no vertigo, it is possible to interchange consciousness and knowing, giving both a rate of 1. The Divisions of Knowing There are divisions of knowing; they include emotion, regulation, feeling, memory, thoughts, intelligence, language, creativity, and so forth. Modulations of all internal senses are knowing processes and are mechanized equally. The kidney is regulated within limits and extents. While this regulation is not in conscious awareness, the outcome of some of the functions [subdivision of regulation] of the kidney is in conscious awareness. The ways 'the brain' gives limits and extents of functions, or to know, is linked with how it does for consciousness, with differences in duration and degree. Seeing is in awareness because the duration of prioritization within an interval is higher, which may spike the degree or intensity of experience. This is different from say the liver, where it can be prioritized during the day, but often shorter prioritization because of the dominance of external senses. Its longer prioritization at night is also in competition with other internal senses that should have their time. When it is said that 'the brain' is involved in consciousness, what exactly in the brain is involved? The hemispheres, the lobes, the center, the gray or white matter, the sulci, the gyri, or what? If a psychiatric patient is given ECT, but paralyzed beforehand, so that the seizures are local to the brain, without the experience in the body, can the activities in the brain be categorized as consciousness alone, since it is the origin and determinant, with the body as the medium? The brain decides and knows. The body doesn't. It is the knowing factor that binds to everything consciousness. It is postulated here that the collection of all the electrical and chemical impulses of nerve cells, with their features and interactions is the human mind. This means that the points of all the outputs of the functions of the brain are impulses, with their features and interactions. Electrical and chemical impulses are always interacting, it is the specifications of these interactions and the features, across centers in the brain that decide what memory, regulation, emotion, feelings, thoughts, intelligence, movement, and so forth are in the cerebral cortex, VTA, putamen, brainstem, cerebellum, and others. There is no key brain function without impulses. This exceeds neural correlates, cellular motifs, or nominal synaptic connections. What would be special about synapses if they don't bear chemical impulses? It is hypothesized here that electrical and chemical impulses are all in loops or sets, across circuits in the brain. It is within these loops that interactions are made, features are displayed and information is organized. It is how consciousness arises. Loops of impulses have different structures, according to what center and what function, but their basic interactive and characteristic pathways are the same. All interactions of impulses in loops help to know, features and degrees differ, but they make to know. Whenever a set of electrical impulses strike a set of chemical impulses, within a loop, they may expand, sharpen, check, or access them. These are done with features, including drifts or stairs, pre-/prioritization, sequences, early-splits or go-before, and a principal spot. Drifts or stairs provide rationing or fills of chemical impulses, where the exact combination of chemical impulses that decide a taste is, differentiating from other tastes, then smell, and so forth. There are dopamine neurons that send axons everywhere. The axon terminals release chemical impulses as part of loops, with rations that determine states or experiences. This is what makes dopamine implicated across functions, but does not make dopamine single in the totality of experiences. What Do They Know? If knowing is interchangeable with consciousness, do LLMs know? What do bionics know? Do robots know? Does generative AI know? There are debates about understanding, emotions, feeling, experience, and so forth that have been said that these technologies do not have, but what do they know in terms of what they do as an output, that in the knowing sphere of humans or of things that exist, they qualify for or pass? If there are things they have that pass for knowing, those things can be sought in the sub-divisions of things humans know, and then rated. If electrodes access biopotentials of the body, those electrodes are surely not conscious, but they are built to know signals—characteristics of human existence. What they do or relate with, they appear to know or they track electrical signals to tell what they [know or] find. If LLMs can answer questions about a road trip in an accurate way that is similar to how a conscious human would answer, LLMs may not have been on one, but they have a shade of human knowing. If robots make their way with computer vision, they may not be seeing, but they navigate their positions and use their landmarks like they know. If generative AI arranges images or videos of scenarios, like an imagination or a dream for a human, they did not experience or understand, but they can pass for knowing. When it is cold and a pet seeks shelter, or when thirsty and the pet seeks water, the pet knows the feeling and what reaction works. The core is knowing. The word consciousness may seem loaded, but when distilled to knowing, what it is or does is clearer. Sentience is also a word that is cautiously used, with many unwilling to extend it to other organisms, however, with knowing, it is not so complex. What does consciousness do? It makes to know. What centralizes sentience? Knowing. What explains consciousness? Knowing. Is there any description of consciousness that is not about knowing? No. Awareness of being or experience is known. Technology has already crossed over to many human domains, and it is strange to keep saying they are nothing when they can pass knowing tests and bear many aspects of what is known, even though there are several others they do not have. What they know can be compared to some . Some LLMs may already have 0.10 as their maximum of what they know. minimums in the subdivisions of memory Bionics and robotics would have systems that are less than that number, but they all have memory that knows in maximums that are still less than the minimums for humans in the memory division. For what consciousness does, knowing is involved. For knowing, what some digital systems output has a measure. To locate sentience, knowing is the channel. Feature image source

Robotics, LLMs: Exploring Consciousness, Sentience for AI Chatbots, and Bionics

রোবোটিক্স, এলএলএম: চেতনা অন্বেষণ, এআই চ্যাটবটগুলির জন্য সংবেদন, এবং বায়োনিক্স

Robotik, LLMs: Erforschung von Bewusstsein, Empfindungsvermögen für KI-Chatbots und Bionik

Robótica, LLM: exploración de la conciencia, la sensibilidad para los chatbots de IA y la biónica

Robotique, LLM : exploration de la conscience, de la sensibilité pour les chatbots IA et de la bionique

रोबोटिक्स, एलएलएम: एक्सप्लोरिंग कॉन्शसनेस, सेंटीएंस फॉर एआई चैटबॉट्स और बायोनिक्स

ロボティクス、LLM: AI チャットボットの意識、感覚、バイオニクスの探求

로봇공학, LLM: AI 챗봇 및 생체 공학을 위한 의식, 감각 탐구

Robótica, LLMs: Explorando Consciência, Senciência para Chatbots de IA e Biônica

Робототехника, магистратура: изучение сознания, чувствительности для чат-ботов с искусственным интеллектом и бионики

Robotik, Yüksek Lisans: Yapay Zeka Sohbet Robotları ve Biyonikler için Bilinci, Duyarlılığı Keşfetmek

Robotics, LLM: Khám phá ý thức, cảm giác đối với Chatbot AI và Bionics

机器人学、法学硕士：探索人工智能聊天机器人的意识、感知和仿生学

The lead image for this article was generated by HackerNoon's via the prompt "robot plastic surgery" AI Image Generator Thanks to the constant development of , robots are getting smarter before our eyes. From simple machines that can only do a strict set of programmed actions, they are turning into colleagues who know how to work alongside real people. As a result, they are becoming more widespread in such critical industries as healthcare: the global market for medical robots has from over $11 billion in 2022 to more than $13 billion in 2023 and is expected to reach nearly $26.5 billion as early as 2027, with a compound annual growth rate of almost 19%. artificial intelligence grown The development of computer vision, motion sensors, high-definition surgical microscopic cameras, remote navigation, analytics, and other solutions also contribute to this growth. Apart from technological advances, several other factors influence the market: an and, consequently, the growing demand for various types of medical services, as well as per capita. aging population increasing healthcare spending Let's dig into three different industry use cases where AI and robotics are helping organizations cope with a labor shortage, meet regulatory needs, and achieve new technological advances. Enabling nursing and rehab through robotic inclusion The lack of healthcare workers was a severe problem even before the pandemic. The global market was of 5.9 million nurses, with another 4 million expected to retire in the next ten years. COVID-19 exacerbated the situation and led to massive nurse burnout. This accelerated the exodus of people from the profession and increased the staff shortage to 13 million. short Robotics can help the industry combat this problem and make life easier for nurses. The global market for robotic nursing assistants was at $1 billion in 2022 and is expected to grow at a CAGR of nearly 15% from 2023 to 2030. According to a WHO report, demand is driven, among other things, by an aging population that needs more care: one in six people will be 60 or older by 2030. estimated And from 2020 to 2050, the number of older people will double to 4.1 billion. Robots based on AI technology are already helping nurses successfully with many daily tasks: for instance, recording patients' medical conditions and delivering food and medicine. They are equipped with voice and motion recognition, cameras, and microphones that collect data about a person. They then pass it on to artificial intelligence algorithms for processing, which allows robots to recognize not only physical injuries (for example, if a patient has fallen out of bed), but even non-verbal signs of depression and anxiety. Some robots have a semblance of emotional intelligence and can work as companions. They use AI to read emotions and social cues, making them better at interacting with humans. As a result, they can deliver news to people, remind patients of appointments, and keep them in touch with family. An of a robotic nurse's assistant is Aeo, a humanoid robot with two arms that can, for example, disinfect surfaces, open doors, and carry things. Aeo's vision and artificial intelligence allow it to determine if patients need medical help. example I also want to make special mention of rehabilitation AI robots, indispensable for patients with impaired motor functions. They help people re-learn the necessary skills and accompany them daily. Rehabilitation robots can monitor a patient's physical abilities and strength using sensors, collect key biological indicators during a session, and transmit them to artificial intelligence algorithms for analysis. This allows the system to adjust to a person's rhythm and, as a result, recover faster. In recent news in this area, the first robotic rehabilitation clinic in Australia to help patients undergoing physical and neurological therapy. Thanks to artificial intelligence, the machines develop interactive games for people after a stroke and patients with spinal cord injury, multiple sclerosis, and CVID. Robots gently move patients' fingers, hands, and arms when they cannot do so on their own and help retrain the brain and gradually regain independent movement. opened Reducing pain and recovery time with AI-assisted robotic surgery Surgery is another area where artificial intelligence can make doctors' jobs a lot easier. The surgical robotics industry has to $3 billion in the past eight years. It will continue to grow in the next decade. Bain has recently found out that more than three-quarters of surgeons in the U.S. are interested in surgical robotics. quadrupled AI-assisted surgery is usually associated with minimally invasive procedures that allow minimal patient damage through small incisions. This leads to fewer complications, less pain and blood loss, quicker recovery, and, eventually, less noticeable scars. Leading health institutions, including Mayo Clinic, already robotic-assisted interventions. Though AI surgery is expensive, the benefits the costs and improve health providers’ bottom line as patients don't have to stay in hospitals for long. perform outweigh Thanks to AI, surgeons can focus on complex aspects of surgery. They control a robotic system, which usually consists of a manipulator with a camera and mechanical arms with attached surgical instruments, at a computer near the operating table. Doctors obtain a detailed visualization of microscopic structures through HD cameras. AI and computer vision ensure the speed and accuracy of the procedure, for example, distinguishing tissue types so the doctor does not nick nerves and muscles. Some highly intelligent AI robots can even perform tasks autonomously, with surgeons monitoring them from the console. AI can also help with preoperative planning. At this stage, specialists plan a surgical intervention based on the patient's medical records and images. Algorithms analyze them together with historical methods and millions of datasets from similar surgeries to identify critical insights and best practices. AI is then used to create a plan according to specific criteria, evaluate what is happening and adjust the strategy in real-time in case of unforeseen events. Surgical robots are evolving all over the world today. For instance, in February 2022, a doctor in Belgium Europe's first prostatectomy using the Hugo robotic surgical system. And the UAE recently that it would execute more procedures with intelligent robots in various surgical specialties. Many hospitals in the UAE are already equipped with the DaVinci XI robot, which allows surgeons to have better angles and visibility. performed announced Maintaining privacy and compliance in aesthetic and beauty sectors The wellness and beauty industry could also benefit from introducing robots controlled by AI, especially considering the current recruitment on the market. Smart robots can take over routine operations, staying compliant with law requirements, to reduce the burden on human specialists and free their time for customers in line. crisis Robots greatly simplify hair transplants, which are in high demand today. ISHRS that about 703 thousand of such procedures were performed in 2021. For a transplant, a specialist must extract thousands of hair follicles from the donor site and move them to the required place. This is a lengthy and repetitive process. Modern clinics implement AI robots for these purposes. For example, the American ARTAS uses several AI algorithms to select and take the most robust and prolific follicles automatically. With the help of the ARTAS robot, it is possible to up to 6,000 follicles in one day. With manual extraction, this figure is usually at most 3,000-3,500. estimates extract Another service at the interface between medicine and beauty that AI robotics could change is massage. The pandemic hit the industry hard: in 2020, at least temporarily, 4 out of 5 massage therapists providing their services. Many did not return to work later because they had time to change careers. Meanwhile, educational institutions cannot meet the market demand for massage therapists. stopped As someone familiar with the spa industry, I believe the shortage of specialists in this field is concerning. With more than 70% of salons and hotels struggling to fill vacancies, as shown in the latest ISPA IS survey, it is evident that there is a significant demand for skilled individuals, particularly massage therapists. The inability to find qualified practitioners could potentially impact the quality of services provided to customers. Robots based on AI could offer high procedure accuracy, eliminate human errors, and reduce the cost of a massage by up to 40%. They can work together with humans and provide services to those clients who cannot wait to see a real specialist or do not like the touch of strangers. There are already several AI-robotic massage solutions on the global market. Robosculptor, for example, has a modular design with a couch, a control screen, an automatic drive, and a high-speed 3D scanner. The system scans the patient's body position with a high-resolution 3D camera in 10 seconds, creates a 3D model of the patient, and builds an automatically adaptable map of the trajectories the massage session will follow. In this case, AI improves more accurate 3D body recognition based on the experience of scanning multiple persons. A common concern, as always with robots, is patient safety. In robotic massage, it can be guaranteed, for example, by limiting the maximum load on the body, joysticks to regulate the pressure level within certain limits, and alarm buttons for the client and the operator. Future of AI Robotics in Healthcare and Beauty According to forecasts, robotics in medicine and beauty will continue to develop rapidly. The market for AI in robotics will even faster, with CAGR exceeding 27% over the next 5 years. Thanks to AI, robots could revolutionize the segment and take on more and more functions. I firmly believe it has the potential to automate and improve a wide range of processes, from direct patient care to mass drug production. In surgery, intelligent robots will be able to perform some procedures completely autonomously, such as suturing. AI-transplantation technology could be adapted to Botox or filler injections. To do this, AI has to learn how to perform these procedures in a way that doesn't mess up a person's face. grow So far, the biggest obstacle to the development of AI in is the cost of the technology, which does not pay for itself immediately, as well as a certain distrust of it by vendors and patients. Building trust can be the key to innovation. Hospitals and other institutions need to explore and communicate to the market the benefits that smart robots can bring to both patients and healthcare providers. It will also be necessary for clinics and salons to establish a close connection between vendors and their staff to teach the latter how to handle AI properly. robotics

From Massage to Surgery: How Robotics and AI Are Changing Medicine And Beauty 

Del masaje a la cirugía: cómo la robótica y la IA están cambiando la medicina y la belleza

Du massage à la chirurgie : comment la robotique et l'IA changent la médecine et la beauté

मसाज से लेकर सर्जरी तक: कैसे रोबोटिक्स और एआई चिकित्सा और सौंदर्य को बदल रहे हैं

マッサージから手術まで: ロボット工学と AI が医療と美容をどのように変えるか

Da massagem à cirurgia: como a robótica e a IA estão mudando a medicina e a beleza

Từ mát-xa đến phẫu thuật: Người máy và trí tuệ nhân tạo đang thay đổi y học và sắc đẹp như thế nào

从按摩到手术：机器人技术和人工智能如何改变医学和美容

It’s no secret that the medical and health care industries are both ripe for innovation. New and advanced technologies are constantly emerging and are capable of transforming medicine in ways never seen before. Two prevalent technologies – robotics and automation – are carving out places in medicine. As the robotics and automation markets continue to grow, the number of medical use cases will increase. Robotics and automation solutions will benefit the medical fields, specifically in immunohistochemistry (IHC) laboratories. Below, learn about IHC labs, the robotics and automation markets, and how robotics and automation solutions could benefit modern IHC labs. What Are Immunohistochemistry (IHC) Labs? Immunohistochemistry (IHC) is defined as a diagnostic technique that occurs in a closed laboratory setting. In an IHC lab, skilled pathologists use advanced technology to perform to diagnose certain diseases. special tests on biopsy tissue samples Tests in an IHC lab are incredibly precise and help a patient’s care team learn specific details about their disease to determine treatment options. For example, an IHC lab can determine where cancer started, the type of cell it started in, and whether cancer will grow slowly or quickly. This detailed information is invaluable to medical professionals and their patients. Aside from diagnostics, IHC is also used for biological research and drug development. There’s a greater demand for IHC in clinical diagnostics. With , it can be challenging for labs to meet that increasing demand. ongoing staff shortages As a result, some IHC labs are investing in or considering investing in robotics and automation to overcome these challenges and operate more efficiently. Since robotics is already a booming industry, more robotics engineers and programmers will likely be needed in the medical field and other sectors. Use Cases for Robotics and Automation in IHC Labs Before getting into use cases, it is important to define robotics and automation. The two terms are linked but have different meanings. Here are : their simple definitions Robotics: The design, creation, and use of mechanical robots to accomplish specific tasks, often those that would otherwise require human action. Automation: Using self-operating, mechanical machines, computer software, or other technologies to perform human tasks. A key to automation is that the device follows a set of predetermined rules and instructions for operation. An IHC test involves determining whether a piece of tissue (the biopsy sample) expresses a particular protein. Pathologists can use . Still, learning where the protein is within the tissue requires better visualization. mass spectrometry or western blotting techniques Processes in an IHC lab are time-consuming and subjective, meaning results can vary considerably. IHC labs are looking into robotics and automation solutions to minimize variability and improve other process pain points. Below are examples of how robotics and automation play an increasingly important role in IHC labs. Example of Robotics in IHC Labs Most robots in IHC labs are called auto-samplers. Their primary use is continuously providing samples for analytical devices in the lab. ThermoFisher Scientific, Epredia, and Lab Vision are three companies that offer robotics used in IHC labs. These robotic solutions are called auto-stainers – space-saving, fast, and reliable tools that can help with IHC processes. Two examples of are the Telepathology and Lab Vision Auto-Stainer 480S-2D and 360-2D. auto-stainers in IHC labs With these robotics tools, labs are increasing throughput, improving accuracy and precision, and eliminating some of the manual work needed in the lab. Example of Automation in IHC Labs IHC automation can alleviate some of the workloads for pathologists in a lab. The is a new machine used in many IHC labs. Dako Omnis Dako Omnis is an automated system capable of IHC and fluorescence in situ hybridization (FISH) staining and essentially treats every slide the same. By leveraging this automated solution, pathologists and lab technicians can spend more time on other important tasks. Another solution – the SNAP i.d. IHC system, created by MilliporeSigma – , which increases the efficiency of slide-staining processes. involves a vacuum manifold Additionally, it can make it easier for pathologists to reproduce manual slide staining. Researchers can process and analyze around 24 slides simultaneously due to the vacuum suction. Robotics and automation have already improved the efficiency of IHC labs, which are currently facing labor shortages and tedious processes. The Importance of New Tech in IHC Labs While robotics and automation solutions are expected to become more widely used in IHC labs and beyond, other technologies will likely emerge to improve their operations. There’s no denying that IHC labs perform significant tasks, such as developing life-saving drugs or diagnosing cancer. Labs must employ any relevant technological solutions to overcome ongoing challenges.

Using Robotics & Automation in IHC Labs

Uso de la robótica y la automatización en los laboratorios IHC

Utilisation de la robotique et de l'automatisation dans les laboratoires IHC

IHC लैब्स में रोबोटिक्स और ऑटोमेशन का उपयोग करना

IHC ラボでのロボティクスと自動化の使用

Usando robótica e automação nos laboratórios da IHC

Sử dụng Robotics & Automation trong IHC Labs

在 IHC 实验室中使用机器人和自动化

Hello, everyone! My name is Anton. And I want to share the story of my first steps into robotics and particularly about my Raspberry Pi + ESP32 drone. (I also made a video about it and attached it at the end of the article) Preface At the beginning of the story I was working as a web developer (React, Typescript, etc), had some C++ knowledge, as for electronics skills — I could solder ripped headphone wires. The problem was that being a web developer I started to feel dissatisfied because what I do doesn’t affect the physical world in any meaningful way. Of course, there are a lot of software-only projects that impact the real world but I decided to take the most straightforward approach and dive into robotics. So I read some books on the subject, watched some videos on youtube, read some articles, and bought soldering equipment and a 3D printer, as 3D printers are cheap and available nowadays and can spare me the headache of making parts out of food containers and similar junk. The first robot My first robot was this caterpillar platform: I designed parts in Blender and then printed them. I used Raspberry Pi as its brain and a power bank as a supply. I soldered the motor controller out of relays and transistors. In the process of making this thing I encountered one problem, when I assembled everything, the motors were barely moving. It turned out I mixed up the collector and emitter of the transistors, and after resoldering them everything started to work. It’s hard to call this thing a robot because it doesn’t do anything on its own and is only remotely controlled. Anyway, I felt great about making it, believed in myself, and decided to make the same thing but a flying one. The second robot After some research, I decided to use some conventional quadcopter parts such as brushless motors, electronic speed controllers, and a lipo battery capable of providing high current to the motors. As a brain, I still used the same Raspberry Pi. After I found out how to interface Raspberry Pi with ESCs I started to design a frame to hold all the parts. I was doing that relying only on intuition in the hope that requirements for a frame are not very strict because I saw drones of such weird shapes that were still able to fly. I also bought a board incorporating a gyroscope, accelerometer, barometer, and magnetometer on the i2c bus. Before I even learned what a PID controller is, my first intuitive approach was similar to the way its P component works — just increase or decrease motor speed proportionally to the angle the drone diverges from the horizontal plane. In the beginning, I thought that it wouldn’t be a very difficult task but after all the failed attempts I had no idea how to do this the right way because there are so many things that may not work properly. And contrary to making something software-only, every run may end up with physical damage and the next try will be possible only after damaged parts are replaced. Also, it is not that easy to test this thing in a small rented flat. Had I had a big garage, shed, or something like this I could have made construction that would restrict the drone’s motion. Instead, I had to improvise and use shoelaces and my own hands which sometimes led to minor but unpleasant injuries. At some point, I thought that it is not convenient to control the drone with a mouse and keyboard and decided to make a controller app, where I can change parameters, monitor sensor readings, and gradually control the drone with analog-like UI. Initially, for some reason, I focused on horizontal leveling control just to realize that I cannot safely test it and what I needed more is automatic height control. My first thought was to use barometer readings but they were not precise enough to control height indoors (or maybe I just used them in the wrong way) so I bought an ultrasonic sensor in a local electronic component shop. Actually, it had a weird interface that turns one of the pins high during measurement and you have to count time to deduce the distance. When the Raspberry Pi was relatively idle it worked fine but under computational load it just missed timings and the readings were garbage. So I had to add an additional AVR controller to the project solely to measure these timings and output results in convenient digital form on the SPI interface. I also bought two bicycle brake cables and printed guides for the drone to restrict its motion to up and down so I could focus on height control without thinking about controlling other degrees of freedom. And it kinda worked except for the case when propellers were working, creating turbulence which interfered with ultrasonic sensor readings at heights above 40cm. When I finally found it out I had to replace the ultrasonic sensor with a laser one. At that point, I already had height control but still couldn’t achieve stable horizontal leveling, even though I had implemented PID control for it. I guessed several reasons for it and I am not sure which of them contributed to the problem the most. But here they are: For the first one and probably the most significant, I used cheap propellers (and even tried to print them) and neglected the necessity to balance them, which in turn overloads IMU sensors with noise which reduces their precision. When I finally decided to address this issue I added a feature to my app to take the last 100 gyroscope readings and output range between the biggest and smallest value. The bigger the range when the propeller is working the more unbalanced it is. I randomly stuck duct tape to the blades of propellers, then run them to check the value, if it is increased — probably a bad place to stick there, if the noise is decreased — I am on the right path, then I tried to stick it further or closer to the center and check again, so this way I reduced the noise on each propeller. The second possible issue is that Raspberry Pi only has only two PWM channels, but there are four ESCs controlled with it, and software PWM may be not precise enough and also wastes CPU cycles. So I decided to use ESP32 for this task because they are cheap, easily available, and have a lot of interfaces that Raspberry Pi lacks. I started with controlling ESCs through ESP32 connected with Raspberry Pi over SPI and ended up moving all the flight controller logic to ESP32. The third issue was probably not a rigid enough frame, which I redesigned and printed several times. Another issue is suboptimal coefficients for the complementary filter I used for estimating incline angle. The optimal is 0.999 for the angle estimated by integrated gyroscope readings and 0.001 for the angle measured by the accelerometer. I didn’t consider that pitch turns into roll and vice versa if the inclined drone is rotated around its local z-axis. Very slow oscillations of incline angle may be fixed by increasing the P coefficient several times instead of decreasing it as some sources suggest. Somewhere in the development process, I got frustrated with the number of wires between the modules and the way they were placed. So I decided to invest some time in the skill of making custom PCBs. I thought about it for a long time but was discouraged by the necessity of buying expensive tools and dealing with chemicals. Actually, it turned out easier than I thought, I watched some tutorials on KiCAD to create the design of my PCB. I bought a second-hand laser printer for 40$, also bought a regular iron without holes for 5$, and the most expensive tool was a drilling press for 75$. The chemicals turned out to be not so dangerous, they won’t dissolve your skin if you touch them. So I made this PCB on the fourth attempt. The hardest part was actually applying the solder mask. What this PCB does is that it interconnects sensors, ESP32, and Raspberry Pi and delivers appropriate voltage to different parts. The fancy charts which show current sensor readings in the app turned out to be difficult to analyze while trying to prevent the drone from crashing. So I added the ability to record the sensor data and wrote a python script to analyze it frame by frame with the video record of the flight. Had I done that earlier I would have spent much less time investigating problems caused by the faulty ultrasonic sensor which I mentioned previously. At some point, I achieved relatively stable height, direction, and horizontal leveling control but still couldn’t avoid horizontal drifting. And if someone knows is it possible to get rid of horizontal drifting using only IMU sensors, please, let me know in the comments. I did some research and I found out that some drones use GPS for position hold and I tried to implement the same technique. But the problem is that it works only outside, but even outside I couldn’t achieve any satisfactory results. The weather was terrible that season with wind, rain, and snow, so I didn’t make many attempts, bailed with GPS, and tried a computer vision approach. I placed a camera on the bottom of the drone and tried to hold this blue mark on the floor using OpenCV library. After several attempts and tweaks here is the result: https://www.youtube.com/watch?v=poqQmvoBlP8 Now it can hover at one point without manual control and without any strings. This was my intermediate goal which I finally achieved, so I decided to document and share my progress. I also added the transfer of a video stream from the drone to the app, so now I can experiment with computer vision to try different approaches to make the general position hold without special marks on the floor and to make other features. In the current configuration, the drone is made like this: ESP32 is responsible for height, direction, and leveling control. It takes data from IMU sensors and a laser distance sensor on the bottom. It gives commands to motor controllers and also it takes commands from raspberry pi, which itself is responsible for communication with the app and position control using data from the camera. If someone is interested to see the code ESP32 part Raspberry Pi part Client android app The code isn’t very clean, as I changed implementation frequently trying to make it work properly. Maybe I will clean it up in the future :) Also, I made a video about it: https://www.youtube.com/watch?v=3mAFEW0YOJ0 Thanks for your attention! Cheers!

Building a Raspberry Pi + ESP32 Drone: My First Steps Into Robotics

Construcción de un dron Raspberry Pi + ESP32: mis primeros pasos en la robótica

Construire un drone Raspberry Pi + ESP32 : mes premiers pas dans la robotique

रास्पबेरी पाई + ईएसपी 32 ड्रोन का निर्माण: रोबोटिक्स में मेरा पहला कदम

Raspberry Pi + ESP32 ドローンの構築: ロボット工学への第一歩

Construindo um drone Raspberry Pi + ESP32: meus primeiros passos na robótica

Xây dựng Máy bay không người lái Raspberry Pi + ESP32: Những bước đầu tiên của tôi để trở thành người máy

构建 Raspberry Pi + ESP32 无人机：我进入机器人技术的第一步

Boaz Shor: The Engineer Behind $80M in AI Revenue and the Future of Robotics

Rahul Gangolli, a Senior Technical Program Manager at Amazon, has established himself as a leader in robotics and manufacturing operations. With a Master’s in Industrial Engineering from Northeastern University and a MicroMasters in Business Management from the Indian Institute of Management, Bangalore, Rahul brings deep expertise in Lean Manufacturing, operational readiness, and cross-functional collaboration. Robotics and manufacturing programs serve as the backbone of modern industrial innovation, driving efficiency, scalability, and adaptability. These programs integrate cutting-edge technologies like robotics, automation, and advanced data analytics to optimize production and support systems, ensuring businesses stay competitive in an ever-evolving market. His ability to launch scalable programs, optimize processes, and translate complex projects into actionable deliverables has positioned him as a strategic leader in the field. Inspiration and Early Beginnings Rahul’s journey into robotics and manufacturing began with a personal experience that shaped his career aspirations. At 14, he witnessed his grandfather struggle with Parkinson’s disease, which inspired him to envision assistive technologies. “That experience solidified my determination to create innovative solutions that bridge the gap between human needs and technological advancements,” he shares​. This passion for problem-solving led Rahul to specialize in robotics and manufacturing, fields where he could leverage his technical acumen to drive impactful solutions. His academic foundation in industrial engineering provided the tools to develop systems that enhance efficiency and scalability, while his exposure to pioneering technologies inspired him to remain at the forefront of innovation. Leadership in Robotics and Manufacturing Programs Rahul’s leadership is characterized by his data-driven decision-making, innovative problem-solving, and systems-thinking approach. At Amazon, he spearheaded the launch of 86 new facilities in 2021, achieving 100% operational readiness while ensuring high safety and efficiency standards. “Data transparency and precise metrics were critical in optimizing workflows and identifying areas for improvement,” Rahul explains​. His problem-solving acumen has resulted in high-impact initiatives such as developing a scalable retrofit process that improved robotics performance by 25% and saved $15 million. By standardizing upgrade protocols and integrating modular components, Rahul has extended the operational lifespan of robotics systems, demonstrating his ability to balance technical complexity with business goals. Driving Cross-functional Collaboration Effective collaboration is at the core of Rahul’s leadership philosophy. He fosters alignment among diverse teams, from engineering and operations to support and deployment. Rahul emphasizes the importance of structured communication strategies, including regular cross-functional reviews and centralized dashboards for real-time updates. “Clear communication and defined roles prevent misalignments and ensure that every team is working toward the same goal,” he notes​. Through mentorship and team development, Rahul has built high-performing, cross-functional teams. His efforts have not only accelerated project delivery but also cultivated a culture of accountability and innovation. “By empowering individuals and fostering open communication, we’ve achieved significant milestones while maintaining operational excellence,” he adds​. Balancing Efficiency and Sustainability Sustainability is a critical focus in Rahul’s work. His initiatives, such as implementing sustainable packaging and eco-friendly manufacturing processes, have led to a 25% reduction in material waste and improved recyclability rates. Rahul believes in integrating sustainability into every stage of manufacturing, aligning operational goals with environmental responsibility. By leveraging technologies like AI-driven optimization and renewable energy, Rahul has minimized the carbon footprint of manufacturing facilities. “Sustainability isn’t just a goal; it’s a driver of innovation that enhances efficiency and future-proofs operations,” he states​. Innovations Shaping the Future Rahul is particularly excited about emerging trends such as collaborative robotics, additive manufacturing, and digital twins. These technologies promise to revolutionize the scalability and precision of manufacturing operations. He envisions incorporating AI-powered analytics for predictive maintenance, IoT-enabled smart manufacturing systems, and modular production lines to enhance flexibility and responsiveness. “Staying ahead of technological advancements is key to maintaining competitive advantage and driving systemic change in the industry,” Rahul explains​. His forward-looking approach ensures that Amazon’s robotics and manufacturing operations remain leaders in innovation. Rahul Gangolli’s expertise in robotics and manufacturing programs exemplifies the qualities of a strategic leader. By integrating emerging technologies, fostering collaboration, and aligning operational excellence with sustainability, Rahul has delivered transformative results that set new benchmarks for the industry. His work not only strengthens Amazon’s technical operations but also serves as a model for scalable and sustainable practices, solidifying his reputation as a pioneer in the field. Rahul Gangolli, a Senior Technical Program Manager at Amazon, has established himself as a leader in robotics and manufacturing operations. With a Master’s in Industrial Engineering from Northeastern University and a MicroMasters in Business Management from the Indian Institute of Management, Bangalore, Rahul brings deep expertise in Lean Manufacturing, operational readiness, and cross-functional collaboration. Robotics and manufacturing programs serve as the backbone of modern industrial innovation, driving efficiency, scalability, and adaptability. These programs integrate cutting-edge technologies like robotics, automation, and advanced data analytics to optimize production and support systems, ensuring businesses stay competitive in an ever-evolving market. His ability to launch scalable programs, optimize processes, and translate complex projects into actionable deliverables has positioned him as a strategic leader in the field. Inspiration and Early Beginnings Inspiration and Early Beginnings Rahul’s journey into robotics and manufacturing began with a personal experience that shaped his career aspirations. At 14, he witnessed his grandfather struggle with Parkinson’s disease, which inspired him to envision assistive technologies. “That experience solidified my determination to create innovative solutions that bridge the gap between human needs and technological advancements,” he shares​. “That experience solidified my determination to create innovative solutions that bridge the gap between human needs and technological advancements,” This passion for problem-solving led Rahul to specialize in robotics and manufacturing, fields where he could leverage his technical acumen to drive impactful solutions. His academic foundation in industrial engineering provided the tools to develop systems that enhance efficiency and scalability, while his exposure to pioneering technologies inspired him to remain at the forefront of innovation. Leadership in Robotics and Manufacturing Programs Leadership in Robotics and Manufacturing Programs Rahul’s leadership is characterized by his data-driven decision-making, innovative problem-solving, and systems-thinking approach. At Amazon, he spearheaded the launch of 86 new facilities in 2021, achieving 100% operational readiness while ensuring high safety and efficiency standards. “Data transparency and precise metrics were critical in optimizing workflows and identifying areas for improvement,” Rahul explains​. “Data transparency and precise metrics were critical in optimizing workflows and identifying areas for improvement,” His problem-solving acumen has resulted in high-impact initiatives such as developing a scalable retrofit process that improved robotics performance by 25% and saved $15 million. By standardizing upgrade protocols and integrating modular components, Rahul has extended the operational lifespan of robotics systems, demonstrating his ability to balance technical complexity with business goals. Driving Cross-functional Collaboration Driving Cross-functional Collaboration Effective collaboration is at the core of Rahul’s leadership philosophy. He fosters alignment among diverse teams, from engineering and operations to support and deployment. Rahul emphasizes the importance of structured communication strategies, including regular cross-functional reviews and centralized dashboards for real-time updates. “Clear communication and defined roles prevent misalignments and ensure that every team is working toward the same goal,” he notes​. “Clear communication and defined roles prevent misalignments and ensure that every team is working toward the same goal,” Through mentorship and team development, Rahul has built high-performing, cross-functional teams. His efforts have not only accelerated project delivery but also cultivated a culture of accountability and innovation. “By empowering individuals and fostering open communication, we’ve achieved significant milestones while maintaining operational excellence,” he adds​. “By empowering individuals and fostering open communication, we’ve achieved significant milestones while maintaining operational excellence,” Balancing Efficiency and Sustainability Balancing Efficiency and Sustainability Sustainability is a critical focus in Rahul’s work. His initiatives, such as implementing sustainable packaging and eco-friendly manufacturing processes, have led to a 25% reduction in material waste and improved recyclability rates. Rahul believes in integrating sustainability into every stage of manufacturing, aligning operational goals with environmental responsibility. By leveraging technologies like AI-driven optimization and renewable energy, Rahul has minimized the carbon footprint of manufacturing facilities. “Sustainability isn’t just a goal; it’s a driver of innovation that enhances efficiency and future-proofs operations,” he states​. “Sustainability isn’t just a goal; it’s a driver of innovation that enhances efficiency and future-proofs operations,” Innovations Shaping the Future Innovations Shaping the Future Rahul is particularly excited about emerging trends such as collaborative robotics, additive manufacturing, and digital twins. These technologies promise to revolutionize the scalability and precision of manufacturing operations. He envisions incorporating AI-powered analytics for predictive maintenance, IoT-enabled smart manufacturing systems, and modular production lines to enhance flexibility and responsiveness. “Staying ahead of technological advancements is key to maintaining competitive advantage and driving systemic change in the industry,” Rahul explains​. His forward-looking approach ensures that Amazon’s robotics and manufacturing operations remain leaders in innovation. “Staying ahead of technological advancements is key to maintaining competitive advantage and driving systemic change in the industry,” Rahul Gangolli’s expertise in robotics and manufacturing programs exemplifies the qualities of a strategic leader. By integrating emerging technologies, fostering collaboration, and aligning operational excellence with sustainability, Rahul has delivered transformative results that set new benchmarks for the industry. His work not only strengthens Amazon’s technical operations but also serves as a model for scalable and sustainable practices, solidifying his reputation as a pioneer in the field.

Rahul Gangolli’s Leadership in Robotics and Manufacturing Programs

قيادة راؤول جانجولي في برامج الروبوتات والتصنيع

Лідэрства Рахула Ганголі ў праграмах робататэхнікі і вытворчасці

রাহুল গঙ্গোলির রোবোটিক্স এবং ম্যানুফ্যাকচারিং প্রোগ্রামে নেতৃত্ব

Liderazgo de Rahul Gangolli en programas de robótica y fabricación

Rahul Gangollin johtajuus robotiikassa ja valmistusohjelmissa

Kepemimpinan Rahul Gangolli dalam Program Robotika dan Manufaktur

La leadership di Rahul Gangolli nei programmi di robotica e produzione

ロボット工学と製造プログラムにおけるラフル・ガンゴリのリーダーシップ

Рахул Ганголлинин робототехника жана өндүрүш программаларындагы лидерлиги

Ubuyobozi bwa Rahul Gangolli muri Roboque na Gahunda yo Gukora

Uongozi wa Rahul Gangolli katika Mipango ya Roboti na Utengenezaji

ความเป็นผู้นำของ Rahul Gangolli ในโครงการหุ่นยนต์และการผลิต

The robotics industry is experiencing a profound transformation, fueled by artificial intelligence (AI). Traditional robots, which rely on rigid programming, are evolving into adaptive systems capable of responding to dynamic environments. I spoke with Ismail Karankin, the founder of Oli Robotics, a startup developing reinforcement learning and LLM-based software for industrial robot manipulators. Their mission: to revolutionize workplace safety by delegating hazardous tasks to robots. How did you start implementing AI in manufacturing? What inspired the creation of Oli Robotics? I founded Oli Robotics because I saw immense potential at the intersection of AI and robotics. The falling costs of AI technologies, driven by advancements in GPUs and tensor processing units (TPUs), have made robotics more accessible. At the same time, industrial robot prices are dropping, with over 200 brands of robotic manipulators emerging in China over the past five years, intensifying competition. However, software for these manipulators is still stuck in the past. Most rely on trajectory planning and sensor data. Programming a six-axis manipulator involves calibrating its coordinates, setting angular constraints, and designing motion trajectories—an inflexible process suited only to static environments. Tesla encountered this issue in 2018 when scaling production: minor changes in manufacturing required costly reprogramming of robotic systems. With AI, robots can adapt in real-time, adjusting their actions based on sensor feedback. Instead of pre-calculating fixed trajectories, our system dynamically recalibrates, improving efficiency and minimizing human intervention. Over time, robots learn to optimize their actions, improving assembly quality without manual reprogramming. At Oli Robotics, we are creating adaptive software that reacts to environmental changes, enabling robots to adjust their movements on the fly. While this was once cost-prohibitive due to computational demands, the decreasing costs of LLMs and AI infrastructure are turning this vision into reality. What opportunities does AI bring to manufacturers? AI is already revolutionizing industries by predicting equipment failures and identifying defects in production lines. But the most significant disruption, I believe, will come from humanoid robots. Unlike specialized robots, humanoids are designed to interact with existing tools and environments, making them highly versatile. Their human-like form enables them to work in conditions where human skills are typically required. Beyond manufacturing, humanoids can integrate into almost every aspect of human activity. The cost of humanoid robots is also becoming more accessible. For instance, Unitree’s G1 humanoid robot starts at $20,000, making them a feasible investment for many industries. You previously founded a drone delivery startup. What lessons did you learn from that venture? In 2016, I launched Airpost, a startup focused on delivering small parcels using drones. We even made it into Startupbootcamp as one of the top 1,100 projects. The idea was simple: drones could deliver packages up to 4 kg faster by avoiding traffic and eliminating the need for a pilot. However, we faced significant regulatory hurdles. At the time, drones weighing over 450 grams required registration, and fully autonomous drones were prohibited. For example, a client—a biolab in a smaller town in Eastern Europe—wanted to use drones to quickly transport samples to nearby capitol city. But the legal landscape made such operations impractical. One amusing yet sobering incident involved a request for pinpoint delivery accuracy within one meter. The caller eventually revealed they intended to use the drones to smuggle phones into a restricted area. Naturally, we declined the offer. After a year of challenges, we decided to close the startup. Do you see risks in the increasing use of AI technologies? One common concern is that humanoid robots could replace human jobs. However, the reality has often been the opposite. In the U.S., for example, companies are hiring more people to prepare training data for AI models. The real bottleneck in AI development is the availability of high-quality data. While the internet seems vast, the amount of suitable training data is finite. Companies are now investing heavily in creating and curating datasets to fuel the next generation of AI models. What challenges and milestones have you faced so far with Oli Robotics? The biggest challenge is convincing manufacturers to adopt adaptive robotics. Traditional robots excel in static, predictable environments, but they struggle with variability. With our software, robots learn and improve in real-time, making them suitable for dynamic tasks. One of our most promising collaborations is with a bakery in San Jose. Employees currently face high risks of burns when loading and unloading bread due to extreme temperatures. Our adaptive robot manipulators are designed to handle such tasks, ensuring both safety and efficiency. We’ve also been recognized by the Silicon Valley Residency accelerator as one of the top 30 startups from Central Asia. This validation, along with interest from five potential clients, reinforces our belief in the transformative power of our technology. What do you think is the future of robotics in the workplace? I believe we’re on the brink of a robotics revolution. Adaptive AI-driven robots will replace humans in hazardous roles, reducing workplace injuries. At the same time, advancements in humanoid robots will expand their use cases, making them invaluable in industries ranging from healthcare to logistics. Ultimately, AI and robotics aren’t just about replacing humans—they’re about augmenting human capabilities, creating safer workplaces, and enabling industries to operate more efficiently. The robotics industry is experiencing a profound transformation, fueled by artificial intelligence (AI). Traditional robots, which rely on rigid programming, are evolving into adaptive systems capable of responding to dynamic environments. I spoke with Ismail Karankin, the founder of Oli Robotics , a startup developing reinforcement learning and LLM-based software for industrial robot manipulators. Their mission: to revolutionize workplace safety by delegating hazardous tasks to robots. Oli Robotics How did you start implementing AI in manufacturing? What inspired the creation of Oli Robotics? How did you start implementing AI in manufacturing? What inspired the creation of Oli Robotics? I founded Oli Robotics because I saw immense potential at the intersection of AI and robotics. The falling costs of AI technologies, driven by advancements in GPUs and tensor processing units (TPUs), have made robotics more accessible. At the same time, industrial robot prices are dropping, with over 200 brands of robotic manipulators emerging in China over the past five years, intensifying competition. However, software for these manipulators is still stuck in the past. Most rely on trajectory planning and sensor data. Programming a six-axis manipulator involves calibrating its coordinates, setting angular constraints, and designing motion trajectories—an inflexible process suited only to static environments. Tesla encountered this issue in 2018 when scaling production: minor changes in manufacturing required costly reprogramming of robotic systems. With AI, robots can adapt in real-time, adjusting their actions based on sensor feedback. Instead of pre-calculating fixed trajectories, our system dynamically recalibrates, improving efficiency and minimizing human intervention. Over time, robots learn to optimize their actions, improving assembly quality without manual reprogramming. At Oli Robotics, we are creating adaptive software that reacts to environmental changes, enabling robots to adjust their movements on the fly. While this was once cost-prohibitive due to computational demands, the decreasing costs of LLMs and AI infrastructure are turning this vision into reality. What opportunities does AI bring to manufacturers? What opportunities does AI bring to manufacturers? AI is already revolutionizing industries by predicting equipment failures and identifying defects in production lines. But the most significant disruption, I believe, will come from humanoid robots. Unlike specialized robots, humanoids are designed to interact with existing tools and environments, making them highly versatile. Their human-like form enables them to work in conditions where human skills are typically required. Beyond manufacturing, humanoids can integrate into almost every aspect of human activity. The cost of humanoid robots is also becoming more accessible. For instance, Unitree’s G1 humanoid robot starts at $20,000, making them a feasible investment for many industries. You previously founded a drone delivery startup. What lessons did you learn from that venture? You previously founded a drone delivery startup. What lessons did you learn from that venture? In 2016, I launched Airpost, a startup focused on delivering small parcels using drones. We even made it into Startupbootcamp as one of the top 1,100 projects. The idea was simple: drones could deliver packages up to 4 kg faster by avoiding traffic and eliminating the need for a pilot. However, we faced significant regulatory hurdles. At the time, drones weighing over 450 grams required registration, and fully autonomous drones were prohibited. For example, a client—a biolab in a smaller town in Eastern Europe—wanted to use drones to quickly transport samples to nearby capitol city. But the legal landscape made such operations impractical. One amusing yet sobering incident involved a request for pinpoint delivery accuracy within one meter. The caller eventually revealed they intended to use the drones to smuggle phones into a restricted area. Naturally, we declined the offer. After a year of challenges, we decided to close the startup. Do you see risks in the increasing use of AI technologies? Do you see risks in the increasing use of AI technologies? One common concern is that humanoid robots could replace human jobs. However, the reality has often been the opposite. In the U.S., for example, companies are hiring more people to prepare training data for AI models. The real bottleneck in AI development is the availability of high-quality data. While the internet seems vast, the amount of suitable training data is finite. Companies are now investing heavily in creating and curating datasets to fuel the next generation of AI models. What challenges and milestones have you faced so far with Oli Robotics? What challenges and milestones have you faced so far with Oli Robotics? The biggest challenge is convincing manufacturers to adopt adaptive robotics. Traditional robots excel in static, predictable environments, but they struggle with variability. With our software, robots learn and improve in real-time, making them suitable for dynamic tasks. One of our most promising collaborations is with a bakery in San Jose. Employees currently face high risks of burns when loading and unloading bread due to extreme temperatures. Our adaptive robot manipulators are designed to handle such tasks, ensuring both safety and efficiency. We’ve also been recognized by the Silicon Valley Residency accelerator as one of the top 30 startups from Central Asia. This validation, along with interest from five potential clients, reinforces our belief in the transformative power of our technology. Silicon Valley Residency accelerator What do you think is the future of robotics in the workplace? What do you think is the future of robotics in the workplace? I believe we’re on the brink of a robotics revolution. Adaptive AI-driven robots will replace humans in hazardous roles, reducing workplace injuries. At the same time, advancements in humanoid robots will expand their use cases, making them invaluable in industries ranging from healthcare to logistics. Ultimately, AI and robotics aren’t just about replacing humans—they’re about augmenting human capabilities, creating safer workplaces, and enabling industries to operate more efficiently.

The Future of Robotics: AI-Powered Adaptation for Safer Workplaces

The Noonification: Systema Robotica (9/11/2024)

Systema Robotica

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