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Robotics News
Medium
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388
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Will we have chatGPT moment for Robots?
- The Humanoid Summit took place at the Computer History Museum in Silicon Valley, with attendees witnessing groundbreaking advancements in humanoid technology.
- Although many questions about the future of humanoid robots were posed at the event, the most central was whether or not they will have their “ChatGPT moment” — a tipping point that transforms how we perceive and integrate robots into everyday life.
- Discussions on real-world applications like companionship and space exploration were held, as well as insights on capital allocation and mass production.
- Panelists explored the transformative role of humanoid robots, highlighting their potential to assist with repairs, cooperative assembly, and hazardous tasks, reducing risks for human crew members.
- Foundational models for robots were discussed, with Sergey Levine delivering an inspiring keynote on the development of Vision-Language-Action (VLA) models, which go beyond describing images to performing complex tasks based on visual inputs.
- Clone Robotics is redefining human-like dexterity by developing bimanual humanoids equipped with artificial muscles that mimic human skeletal muscles.
- The intersection of robotics and art was explored, showcasing collaborative projects that explore motion not just as functional behavior but as a language of communication, challenging traditional perceptions of robots as purely utilitarian.
- Experts emphasized the importance of collaboration between industry leaders, researchers, and regulatory bodies to create frameworks that prioritize safety, utility, and ethical considerations in humanoid robotics.
- The consensus across the summit suggests that while humanoid robots may one day reach a “ChatGPT moment,” it is more likely to be a gradual evolution rather than a sudden tipping point.
- Every incremental step — whether it’s improved dexterity from bimanual artificial muscles, better safety frameworks, or scalable manufacturing — brings us closer to a future where humanoid robots become integral to factories, industrial environments, and homes.
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Medium
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173
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The Hype is Over: AI Landscape in Venture Capital 2024
- Global AI funding has hit $290 billion over the past five years, with more than 15,400 deals completed since 2022, according to Pitchbook.
- The AI market could grow to $2 trillion by 2030, covering sectors from AI software to hardware and services.
- Large late-stage funding rounds are taking center stage, with established AI companies with proven models raising billions.
- Generative AI startups receive an annual funding contribution of $26 billion for the last five years, particularly across content creation, healthcare, and enterprise solutions.
- There are more than 100 VC funds actively investing in the AI market, with some taking a more cautious approach to revenue and more stable market conditions.
- VCs are focusing less on seed-stage deals as they zero in on startups with established product-market fit.
- AI infrastructure and hardware companies are seeing increased funding as generative AI models demand more computing power.
- VCs are drawn to startups applying AI in healthcare, finance, and defense.
- The next wave of billion-dollar startups in AI could focus on human-AI collaboration, autonomous energy management, and quantum molecular modeling.
- For AI to be a force for good, AI systems need to be built avoiding discrimination and addressing the digital divide.
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Unite
1w
12
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Why Analog AI Could Be the Future of Energy-Efficient Computing
- Artificial intelligence has transformed the way we live, powering tools and services we rely on daily.
- Digital AI demands enormous computational power, consuming significant energy and generating heat.
- Analog AI might be the answer, which promises a more efficient, sustainable path forward.
- AI models, especially complex ones, demand huge amounts of computational power and is incredibly energy-intensive.
- Analog AI uses continuous signals instead of 0s and 1s and are faster, more efficient, and great at handling tasks at once.
- Manufacturing analog circuits is more complex, but advances in material science and circuit design are starting to overcome the issues.
- Analog AI promises to complement digital systems or even replace them in some areas.
- Analog AI offers a way to keep advancing without draining resources and sustainably keep the pace.
- It is an exciting step toward making AI both powerful and sustainable for a better future.
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The Robot Report
1w
42
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Diversity and inclusion can accelerate robotics innovation, finds Max Planck study
- A recent study from the Max Planck Institute for Intelligent Systems highlights the benefits of diversity and inclusion in the field of robotics research.
- The study identifies seven key benefits of diverse and inclusive teams in robotics research, including increased productivity, better handling of complex challenges, creation of disruptive solutions, and addressing bias in technology.
- Diverse teams are also found to create wide-reaching solutions, promote fairness, and enhance employee satisfaction.
- The Max Planck Institute offers guidance on promoting diversity and inclusion within research teams to accelerate progress in robotics.
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The Robot Report
1w
169
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Unleashing the potential of AI in robotics
- In episode 177 of The Robot Report Podcast, Mike Oitzman and Gene Demaitre interview Ben Wolff, co-founder and CEO of Paladine AI. Ben details the company's evolution from hardware-focused robotics to a software-centric approach. The company's robust AI platform, built on years of valuable hardware experience, positions Paladine AI for future growth and scaling their software solutions across diverse industries. Ben shares insights on the challenges faced during this transition and the impact of SPAC acquisitions.
- Paladine AI has developed software capable of teaching robots complex tasks in under 20 minutes. This agnostic software operates seamlessly on various third-party robots, accelerating iteration and development cycles. The company boasts contracts with the Pentagon, securing non-dilutive funding while navigating the inherent compliance challenges.
- Ben highlights the importance of government contracts and the company's hardware-agnostic strategy while looking ahead to future opportunities in automation and AI applications. Meanwhile, General Motors today disclosed that it will no longer fund Cruise LLC's robotaxi deployment work, citing long development times, high costs, and an increasingly competitive robotaxi market as reasons behind its decision. This news comes despite GM pouring more than $10 billion of funding into Cruise.
- Universal Robots (UR) is significantly expanding its market presence in China by opening a production facility and introducing two robots that will be available exclusively to the Chinese market. The company will produce the UR7e and UR12e cobots, which are specially designed to meet the needs of China's automotive, electronic, and metal & machinery industries, alongside others.
- Critical financing apparently didn't come through for Embodied, the creators of the Moxie robot, forcing the company to shut down. You can now submit nominations for the 2025 RBR50 innovation awards that will recognize technology and business innovations in the calendar year 2024. The awards are open to any company worldwide that produces robotics or automation.
- The Robot Report Podcast Episode 177 featuring interview with Ben Wolff, showcases Palladyne AI, a company that has evolved from hardware-focused robotics to a software-centric approach; developing software capable of teaching robots complex tasks in under 20 minutes that can operate seamlessly on various third-party robots.
- Palladyne AI's hardware-agnostic strategy has resulted in contracts with the Pentagon and non-dilutive funding by navigating compliance challenges. General Motors has decided to no longer fund Cruise LLC's robotaxi deployment work, citing long development times, high costs, and an increasingly competitive robotaxi market. Universal Robots (UR) is looking to significantly expand its presence in China by opening a production facility and introducing 2 new cobots (UR7e and UR12e)
- Embodied, the creators of the Moxie robot, have apparently shut down due to critical financing not coming through. The 2025 RBR50 innovation awards recognize technology and business innovations in the calendar year 2024 and are open to any company worldwide producing robotics or automation.
- The awards will recognize primary or applied research in technologies, products, and services; initiatives positioning a company as a market leader or thought leader in the robotics ecosystem; innovations that improve productivity, quality, cost-effectiveness, and automate new tasks, among others. The show is sponsored by FlexQube, which develops unique solutions for material handling, retail, and industrial manufacturing.
- FlexQube's Navigator AMR is the world's first multi-purpose and non-load carrying robot. The system features a standardized coupling interface to connect with different load carriers, depending on the customer's needs. The safety-rated identification of load carrier footprint helps secure a safe and efficient scale-up of different use cases in a factory or warehouse.
- Universal Robots (UR) has established manufacturing capabilities in Nantong, China. They will produce two new cobots for that market: the UR7e and UR12e.
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Unite
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114
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Agent Memory in AI: How Persistent Memory Could Redefine LLM Applications
- Large language models (LLMs) have introduced remarkable advancements in conversational AI, delivering rapid and human-like responses but limited by a drawback with the inability to retain context beyond a single session. The concept of persistent memory, also referred to as agent memory, addresses this limitation by enabling AI systems to retain and recall information over extended periods. Understanding agent memory, it enables AI systems to store and retrieve information from past interactions, remembering conversations, preferences, and patterns. Persistent memory for smarter LLMs.
- Implementing persistent memory in AI entails significant challenges with privacy, bias within AI systems, scalability, and ethical usage but its potential to reshape the future of AI is undeniable. Challenges like scalability, privacy, and bias, the future of AI can become even more promising. Persistent memory is the foundation for more adaptable, intuitive, and impactful AI systems.
- Persistent memory enables AI systems to store and retrieve information from past interactions which leads to smoother, more personalized future interactions. Persistent memory fundamentally changes how LLMs operate. For example, an AI assistant could remember one’s coffee preferences or track ongoing projects. Industries benefit significantly from the application of persistent memory in AI. In healthcare, AI systems equipped with memory can store detailed patient records, including symptoms, treatment plans, and test results.
- Technical implementation of persistent memory in LLMs often involves combining vector databases and memory-augmented neural networks, enabling AI balance retaining long-term data and ensuring fast access to relevant details. The rise of persistent memory has brought significant advancements in the AI industry, hybrid memory systems, new frameworks like MemGPT, and Letta are gaining attention. Persistent memory is bringing innovation across industries, in retail, AI systems enhance shopping experiences by recommending products based on a customer’s purchase history and browsing habits. In entertainment, memory-enabled chatbots are creating immersive storytelling experiences.
- Implementing persistent memory in AI entails significant challenges, but its potential to reshape the future of AI is undeniable. Scalability is one of the most pressing issues. Privacy is another essential concern. Bias within AI systems adds another layer of complexity. Regular audits, diverse datasets, and proactive measures are necessary to ensure fairness and inclusivity in these systems.
- Persistent memory is not just an upgrade for LLMs. Instead, it is a shift that brings AI closer to human-like interactions. The future of AI can become even more promising. By addressing the current challenges, persistent memory can lead to AI systems that are more intelligent, adaptable, and equitable in their applications.
- Looking further ahead, persistent memory could play a vital role in developing Artificial General Intelligence (AGI). AGI must retain and apply knowledge over time to evolve and adapt effectively. The Bottom Line: Persistent memory is a transformative step forward in the AI domain. The evolution makes AI not just a tool but a true partner in forming a smarter, more connected world.
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The Robot Report
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394
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Huawei invests $413M into robotics subsidiary
- Huawei has invested $413 million into its subsidiary Dongguan Jimu Machinery.
- Huawei established Jimu in June 2023 with a registered capital of 870 million yuan.
- Huawei has also launched an embodied AI center in Shenzhen focusing on integrating AI into robots.
- China, with its aim to grow the robotics industry, has become the largest robotics market.
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Unite
1w
186
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Boaz Mizrachi, Co-Founder and CTO of Tactile Mobility – Interview Series
- Boaz Mizrachi is the Co-Founder and CTO of Tactile Mobility. The company specialises in combining signal processing, AI, big data, and embedded computing to enhance smart and autonomous vehicle systems. Its technology enables vehicles to “feel” the road in addition to “seeing” it, optimizing real-time driving decisions and creating accurate, crowd-sourced maps of road conditions.
- Boaz with his team developed new software in the vehicle’s engine control unit (ECU) that allowed to generate new insights like current vehicle weight, tire health, and surface grip through “virtual sensors” that connected to the current vehicle set up and didn't require additional hardware.
- Tactile Mobility uses AI and machine learning to convert processed data from various hardware sensors (wheel speed sensor, accelerometers, steering and brake systems, tire sensors) into real-time insights, or “virtual sensors,” that convey information about the vehicle’s load, grip, and even tire health.
- The System enables adaptive functions, such as adjusting the distance in adaptive cruise control based on the current friction levels or informing drivers that they need to allow more distance between their car and the cars in front of them.
- Tactile Mobility’s technology helps in estimating the friction coefficient between the vehicle and the road resulting in real-time understanding of road conditions, enhancing safety and efficiency.
- Tactile Mobility operates through two technology solutions – VehicleDNA™ and SurfaceDNA™ – Virtual sensors focused on vehicles and surface respectively to provide a clear understanding of the vehicle’s performance limits on any given road, enhancing safety and efficiency.
- By leveraging this vehicle-specific data in the cloud, smart cars will be able to deliver an unprecedented level of precision in sensing and responding to its surroundings. This can lead to smarter cities as vehicles communicate with infrastructure and each other to share real-time insights, ultimately enabling a more connected, efficient, and safer mobility ecosystem.
- Boaz sees the next decade as an expansion of Tactile Mobility’s software in more OEMs globally, offering precise and impactful insights in the automotive industry.
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Unite
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72
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Techno-Panic: Reclaiming Human Value in the Age of Technological Obsession
- Companies in the tech industry are often too quick to adopt new technologies, mistaking adoption for innovation.
- Leaders need to ask how technology positively impacts the people who will be using it.
- The pressure to innovate often leads to poorly informed, costly decisions.
- Business leaders need to understand both the technology and its potential impact on their specific company, customers, employees, and business needs.
- Examples of companies in different stages of technology adoption, like McDonald’s, can help understand the impact of these decisions.
- Smart companies focus on understanding and solving real human problems before considering how technology can scale those solutions.
- The human-centered approach requires leveraging internal teams’ deep business knowledge and subject matter experts who bring fresh perspectives and technical expertise.
- Innovation also requires proper execution after making strategic decisions around technology investment to transform it from a risky gamble into a reliable engine for meaningful growth.
- Prioritizing solving real-world problems over chasing technology helps companies make smarter decisions and build lasting competitive advantages.
- Smart innovation rarely happens in isolation; it thrives through collaboration with those who challenge assumptions, bring fresh ideas, and help bridge the gap between ambition and execution.
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Unite
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80
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Junyoung Lee, President of Technology & Yanolja Group CTO, Co-CEO at Yanolja Cloud – Interview Series
- Junyoung Lee is the President of Technology and Group CTO at Yanolja, as well as the co-CEO of Yanolja Cloud.
- Lee played a pivotal role in driving the company’s digital transformation and global technology strategy.
- He was previously a distinguished career at Google, where he worked for nearly two decades in various roles.
- Lee explains that at Yanolja they prioritise data integrity and the velocity of their Vertical AI solutions.
- Yanolja has introduced AI-driven solutions to improve automation in the travel industry, including their AI-powered customer service tools that reduce response times by 40%.
- To scale AI globally, the company must understand diverse market needs while also leveraging shared insights.
- Vertical AI allows Yanolja to refine data and create industry-specific solutions.
- Lee sees AI revolutionising the travel industry by unlocking the vast potential of underutilised data.
- Yanolja aims to be a unified, efficient travel ecosystem, disrupting the industry by leveraging Vertical AI.
- Lee believes it's essential to have a clear and sustainable vision and mission and foster exceptional teamwork in tech teams to improve people's lives.
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Unite
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312
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3 Core Principles to Drive ROI from GenAI Deployments
- At least 30% of GenAI projects will be dropped after POCs by the end of 2025 due to such issues as poor data quality, insufficient risk controls, fast-growing costs, or an inability to realize desired business value.
- Gartner said GenAI is beginning to enter the trough of disillusionment in its latest Hype Cycle for Emerging Technology, 2024.
- Respondents reported that their GenAI deployments have helped companies notch 15.8% revenue increases, 15.2% cost savings, and 22.6% productivity improvements.
- Rigorously quantify business value, ensure data quality, privacy and security, and strengthen human-GenAI collaboration are key principles to guide the evaluation, selection, and enablement of use cases with GenAI.
- Partners can help enterprises develop detailed business cases by holding workshops to understand overall goals, current state of data processes and infrastructure. They evaluate potential use cases, solve business pains, estimate ROI, and develop KPIs.
- Enterprises must prepare data to ensure AI models generate accurate and reliable outputs. They must implement guardrails and tools to protect sensitive information, including model outputs, from exposure.
- While GenAI will automate some processes, most of the time, it will assist humans in making better decisions. GenAI can provide scenarios and recommendations for decision-makers to consider so that they can optimize outcomes.
- Leaders should take time to train teams on the latest capabilities, define roles and responsibilities clearly, provide guardrails and escalation paths when GenAI doesn’t perform as expected, and stress that they are augmenting human capabilities rather than replacing them.
- Allstate has implemented a GenAI-powered chatbot that delivers real-time, multilingual support and seeks to improve the performance of previous models threefold by identifying those customer journeys that require agent support.
- Leaders can use these three core principles – developing a sound business case, addressing data requirements, and helping teams collaborate with AI – to make new GenAI initiatives successful.
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Embedded
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16
Hot Swap Controllers, Neural-ART Accelerator NPU, Robotics: Embedded Week Insights
- PIMIC launches AI inferencing silicon to enhance edge devices.
- Imec achieves seamless integration of InP chiplets on a 300mm RF silicon interposer.
- Core Ultra 200V processors aim for AI performance and energy efficiency.
- STMicroelectronics unveils STM32N6 MCU series with Neural-ART Accelerator NPU for embedded inference.
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Robotsblog
1w
225
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‘Switch’ by VEX Robotics: Bridging the Gap Between Block Coding and Python
- VEX Robotics has unveiled Switch, a method for teaching computer science. The Switch feature is part of VEXcode, the company’s robot-coding platform, and integrates Python commands within the block-based coding environment. Research suggests block-based coding is ideal for beginner coders but later on, they are more motivated by the authenticity and power of text-based coding. However, this transition is not always easy and is often the reason students do not continue to study Computer Science. The Switch provides educators with a tool to foster a deeper understanding of programming concepts.
- The approach is scaffolded, making the transition from block-based to text-based programming smoother and more intuitive, building proficiency in a single supportive environment. Switch supports different learning paces, including differentiated learning, and builds syntax guidance and helps in reducing errors. It’s an integral part of VEXcode and transforms the learning for students by allowing them to progress from block-based to text-based coding in one platform marking their journey from novice to advanced levels.
- Designed by VEX Robotics, Switch is aimed at improving STEM education in schools. According to Jason McKenna, vice president of global education strategy at VEX Robotics, educators look for ways to teach programming in an approachable manner that enables a student to learn at their own pace, and Switch does just that.
- With Switch, students can learn Python syntax, editing and writing at their own pace, all within the existing block-based environment they are already comfortable using. Students can instantly convert normal blocks into a switch block, giving them the chance to see underlying Python code. Switch allows editing of the Python code and also allows users to write multi-line Python code with proper indentations, all within a Switch block.
- The tool is available for free and compatible with the VEX Robotics IQ, EXP, V5, and CTE workcell. It’s also available with a subscription in VEXcode VR, an online platform that enables students to learn programming by coding Virtual Robots (VR) in interactive, video game-like environments. Fostering continuity in a cohesive environment, Switch reinforces the programming journey from novice to advanced levels.
- VEX Robotics is inviting everyone to try Switch with VEXcode VR or with their VEX hardware in December, as the company is celebrating Computer Science Education Week. The new Hour of Code activities and resources allow students to explore Switch coding across both hardware and virtual platforms.
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The Robot Report
1w
25
Image Credit: The Robot Report
Lam Research’s Dextro cobot boosts semiconductor production efficiency
- Lam Research Corp. has launched Dextro, its first collaborative robotic arm, designed to carry out essential maintenance tasks on semiconductor wafer fabrication equipment. Dextro's precision in tool maintenance is significant for chipmakers to continuously produce at a nanoscale with accurate reassembly at the subsystem level. This leads to less variability and higher yield in production. Within a fab accuracy is vital, and Dextro has been designed to minimize tool downtime and production variability. Nearly every advanced chip built today is made using Lam technology and having Dextro at the fab can enhance yield, even down to the sub-micron level.
- Fabs continue to grow in size, geographic diversity, and equipment complexity, which makes it vital for chip makers to make their processes more efficient by increasing automation as the number of semiconductor positions worldwide continues to outpace the availability of skilled engineers. Precision maintenance is critical as accurate reassembly of subsystems translates to the bottom line. Achieving first-time right (FTR) saves time and cost, repeatable maintenance can reduce waste leading to higher yield in production.
- According to Young Ju Kim, the head of the Memory Etch Technology Team at Samsung Electronics, "error-free maintenance by Dextro helps drive improvements in production variability and yield. This is an exciting milestone in Samsung’s journey to the autonomous fab."
- Dextro is a mobile cart that is guided and docked to a piece of equipment, then operated remotely by a fab engineer or technician. The cart contains the cobot’s control systems and much of the core technology. Within a fab, Dextro's flexibility means it can be repositioned as the process equipment is taken offline for maintenance, making it far more cost-effective than a dedicated cobot for each process tool.
- Dextro cobot arm is made using the Universal Robots' UR5e cobot arm with a reach of 850mm (33.5in.), a payload of 5kg (11lb) and equipped with a quick change end-of-arm wrist. It can accurately install and compress consumable components with over twice the accuracy of manual application, eliminates chamber temperature deviations, which may take a tool out of production and impact die yield.
- Lam Research’s portfolio also includes the Lam Equipment Intelligence process tools with autonomous calibration. It includes the Equipment Intelligence Services that use data, machine learning, artificial intelligence, and Lam domain knowledge to achieve better productivity outcomes.
- Dextro is currently being used in Advanced wafer fabs worldwide and will expand to support more Lam tools beyond Flex G and H series dielectric etch tools in 2025.
- According to Bob O’Donnell, president of TECHnalysis, "Dextro can automate tedious, time-consuming, and often intricate cleaning and maintenance tasks on chip fabrication equipment so that manufacturing output can be maximized. It offers a huge benefit for companies that choose to deploy it."
- Chris Carter, group vice president of the Customer Support Business Group at Lam Research, said, "Dextro is an exciting leap forward in semiconductor manufacturing equipment maintenance. Built to work side by side with fab engineers, it executes complex maintenance tasks with precision and repeatability that are beyond human capability alone, enabling higher tool uptime and manufacturing yield. It is a powerful addition to Lam’s extensive portfolio of tools designed to help chip makers optimize their fabs for cost and productivity."
- As fabs grow in size, geographic diversity, and equipment complexity, chip makers need to optimize their effectiveness of human engineers by increasing automation, especially since the number of semiconductor positions worldwide continues to outpace the availability of skilled engineers.
- Precision maintenance is crucial in tool maintenance since accurate reassembly of subsystems translates into bottom line results, Lam Research revealed. Achieving first-time right saves time and cost as repeatable maintenance can also reduce waste associated with consumable parts, labor, and production downtime. The result is less variability and higher yield in production.
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Medium
1w
310
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Age of a Robot, Resume of a Robot-to Drones
- The age of a robot can be considered as the age of its AI algorithm plus the age of hardware.
- New robots may have AI algorithms that are older, even though they are sold as new.
- The safety of a new robot with an older AI algorithm depends on the manufacturer's testing.
- The skills acquired by a robot, such as walking and picking up objects, determine its resume.
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