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Robotics News
The Robot Report
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Flexiv releases Elements Series 3 to simplify robot simulation, programming
- Flexiv has released Elements Series 3, the latest version of its robot control system.
- The software simplifies the user experience and offers semi-automated features.
- Flexiv's Teach Pendant and simulation tool allow for easy programming and application testing.
- The hardware and software are compatible with all Flexiv robots.
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The Robot Report
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New FANUC heavyweight industrial robot can lift up to 500 kg
- FANUC has introduced the M-950iA industrial robot capable of lifting heavy items up to 500 kg.
- The M-950iA offers a wider range of motion and adaptability for tight workspaces and variable layouts.
- It is designed for rapid and robust performance in heavy part lifting, palletizing, and material-handling tasks.
- FANUC's new robot is set to become a powerhouse for customers seeking adaptable solutions in challenging industrial environments.
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TechCrunch
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Portugal’s Tekever raises $74M for dual-use drone platform deployed to Ukraine
- Dual-use drone startup Tekever has raised €70 million ($74 million) to develop its product and expand into new markets, specifically the U.S.
- The funding round was led by Scottish investment company Baillie Gifford & Co. and the NATO Innovation Fund.
- Tekever's surveillance drones have been used for tracking migrant smugglers in the English Channel and have been deployed in Ukraine.
- The company's approach of controlling all aspects of its operations, from design to software, allows for quick adaptation to customer needs and changing warfare scenarios.
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Analyticsindiamag
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Microsoft Opens First AI, Robotics R&D Lab in Tokyo
- Microsoft has opened its first AI and robotics research and development centre in Tokyo, Japan.
- The lab, named Microsoft Research Asia Tokyo, aims to integrate AI into practical applications across industries like manufacturing and healthcare.
- Microsoft plans to collaborate with Japanese universities and companies to combine Japan's robotics innovations with AI.
- The lab will focus on embodied AI, societal AI, neuroscience and well-being, and industry innovation to develop intelligent systems and drive innovative solutions.
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The Robot Report
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KIMM develops automated mooring system for docking autonomous vessels
- The Korea Institute of Machinery and Materials (KIMM) has developed an automated mooring system for autonomous vessels.
- The system overcomes the limitations of conventional wire-based methods and is expected to be commercially available by 2025.
- It uses vacuum suction pads and a hydraulic system for secure attachment and automated control.
- The technology aims to streamline the mooring process, increasing speed and accuracy while reducing risks and labor needs.
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Mit
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Can robots learn from machine dreams?
- MIT researchers have developed a new training approach to accelerate the deployment of adaptable machines in the real world. The non-invasive system called LucidSim bridges gaps between different technologies. It uses large language models to create various structured descriptions of virtual environments, which then transform into images using generative models. The images reflect real-world physics because of an underlying physics simulator, which is used to guide the generation process.
- Typically, when roboticists want to improve robots' abilities, they invent tasks that push the boundaries of their capabilities. But this process has a scaling problem: the demand for high-quality training data, which is needed to improve the robot, outpaces the humans' ability to provide it.
- LucidSim combines physics simulation with generative AI models to create diverse and realistic virtual training environments. It helps robots achieve expert-level performance in challenging tasks without the need for any real-world data. The system addresses one of the most persistent challenges in robotics, which is the ability to transfer skills learned in simulation to the real world.
- LucidSim's images reflect real-world physics due to an underlying physics simulator, which guides the generation process. LucidSim outperforms the go-to method of domain randomization. Although that technique generates diverse data, it lacks realism.
- The LucidSim could potentially apply beyond quadruped locomotion and parkour, its main test bed, to the mobile manipulation where a mobile robot is tasked to handle objects. The robots still learn from real-world demonstrations at the moment. LucidSim could make data collection easier and more scalable by moving it into a virtual environment.
- One of the challenges in sim-to-real transfer for robotics is achieving visual realism. The LucidSim framework could provide an elegant solution by using generative models to create diverse and highly realistic visual data for any simulation.
- When comparing LucidSim to 'expert training,' where an expert teacher demonstrates the skill for the robot to learn from, the results were surprising. Robots trained by the expert struggled, succeeding only 15% of the time, even when the amount of expert training data quadrupled. But when robots collected their own training data through LucidSim, success rates increased to 88%. "And giving our robot more data monotonically improves its performance — eventually, the student becomes the expert," says Yang.
- The researchers presented their work at the Conference on Robot Learning (CoRL) in early November. Their work was supported, in part, by a Packard Fellowship, a Sloan Research Fellowship, the Office of Naval Research, Singapore’s Defence Science and Technology Agency, Amazon, MIT Lincoln Laboratory, and the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions.
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Medium
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Shanghai Kepler Robotics Revolutionizes Humanoid Robots with Forerunner K2
- Shanghai Kepler Robotics is revolutionizing humanoid robots with its Forerunner K2.
- The Forerunner K2 showcases Kepler's commitment to pushing the boundaries of robotics.
- Kepler's vision of creating collaborative humanoid robots takes a significant step forward.
- The original Forerunner K1 was designed to compete with leading humanoid robots.
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Unite
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Self-Evolving AI: Are We Entering the Era of AI That Builds Itself?
- Self-evolving AI refers to systems that can improve and adapt on their own without needing constant human input.
- Recent breakthroughs in AI have sparked a quest for true self-evolving AI—systems that can adapt and improve on their own, without human guidance.
- Advancements such as automated machine learning, Generative Models in Model Creation, Meta-Learning, Agentic AI, and RL and self-supervised learning have initiated the self-evolutionary process in AI.
- AI could change in unpredictable ways, making it hard to control. To unlock its full potential, we must ensure strict safety measures, clear governance, and ethical oversight.
- Self-evolving AI, when developed fully, could drive breakthroughs in fields like scientific discovery and technology.
- The fear of AI improving itself to the point of becoming incomprehensible or even working against human interests has long been a concern in AI safety.
- Self-evolving AI enables AI to act as a dynamic agent in its development, adjusting and enhancing its performance in real-time.
- To ensure self-evolving AI aligns with human values, extensive research into value learning, inverse reinforcement learning, and AI governance will be needed.
- AutoML systems can now handle complex optimizations more quickly and often more effectively than human experts.
- AI can autonomously enhance its reasoning, expand its knowledge and tackle complex problems.
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Unite
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The Transformative Impact of AI on M&A Dealmaking
- AI is transforming M&A processes by increasing efficiency, mitigating risks, and uncovering new opportunities.
- AI automates manual tasks, allowing dealmakers to focus on strategic decisions and allocate more time to higher-value activities.
- AI assists in identifying potential targets and aids in the valuation process based on historical data and market factors.
- Dealmakers want to use AI tools in the M&A process, but data security and privacy concerns need to be addressed.
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Unite
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Moshe Tanach, CEO and Co-Founder at NeuReality – Interview Series
- Moshe Tanach is the CEO & co-founder of NeuReality, which makes AI affordable and accessible by identifying pain points and providing purpose-built, silicon-to-software AI inference solutions.
- They broke away from big tech giants and disrupted the processor market to develop a better AI architecture that is CPU-free and open, agnostic, and purpose-built for AI inference.
- NeuReality simplifies AI infrastructure deployment, management, and scalability, and enhances business processes and profitability while advancing key sectors such as healthcare, law enforcement, and customer service.
- At Supercomputing 2024, NeuReality will demonstrate an NR1-S Appliance that delivers exceptional efficiency, energy savings up to 15X and cost savings up to 90%, and linear scalability without performance drop-offs.
- NR1 is based on the Programmable Graph Execution Accelerator that simplifies AI model deployment by leading to significant performance gains, while NR1 NAPU is a 7nm SoC offering direct network access for AI pre- and post-processing.
- The patented NR1 AI-Hypervisor optimizes AI task orchestration and resource utilization, and the NR1 AI-over-Fabric Network Engine ensures seamless network connectivity and efficient scaling.
- With NR1, NeuReality's solution is open, accelerator-agnostic, and unlocks AI accelerators to benchmark performance, with an open ecosystem that integrates with any AI inference chip and popular software frameworks.
- Their long-term vision is to make advanced AI affordable and accessible to all, and an open, efficient ecosystem that collaborates with partners to validate their technology across various AI workloads and delivers “inference-as-a-service” and “LLM-as-a-service” through cloud service providers, hyper scalers, and chip makers.
- They enable businesses to overcome the cost and complexity challenges of building and scaling AI systems, and by optimizing the efficiency and performance of any GPU or AI accelerator, they aim to contribute to a win for profit margins, people, and the planet.
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The Robot Report
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ANELLO Photonics secures funding for inertial navigation in GPS-denied environments
- Silicon photonics specialist ANELLO Photonics has completed its Series B funding round for its SiPhOG inertial navigation system, designed for compact, GPS-denied environments. The technology can operate across land, sea and air with position accuracy of around 10 centimetres. ANELLO secured investments from Lockheed Martin, Catapult Ventures, One Madison Group, New Legacy, Build Collective, Trousdale Ventures, In-Q-Tel, K2 Access Fund, Purdue Strategic Ventures, Santuri Ventures, Handshake Ventures, Irongate Capital and Mana Ventures.
- The Santa Clara, California company was founded in 2018, and its SiPhOG - Silicon Photonics Optical Gyroscope - is based on integrated photonic system-on-chip (SoC) technology. The company has more than 28 patents and 44 more pending, and its technologies also include a sensor-fusion engine using artificial intelligence.
- SiPhOG is used with the Global Navigation Satellite System (GNSS) and is suitable for autonomous vehicles, drones and mobile robots. It weighs 20 grams, measures 5.5cm by 2.4cm and uses a 3.3-volt power supply.
- VC investors have reportedly said car makers are installing six separate lidar systems on a vehicle, at $10,000 apiece. ANELLO's optical technology offers navigation across long distances and is scalable, according to the company. SiPhOG is built to work across land, air and sea. New applications include orchards and indoor robots working in construction.
- As well as inertial navigation, the company is developing 3D sensing and ranging, velocimetry and biomedical sensing applications for SiPhOG.
- The device is typically integrated with accelerometers, magnetometers, and triple-redundancy MEMS gyroscopes.
- Co-founder and CEO Dr Mario Paniccia said: "We have self-alignment correction, and within 15 minutes, you can have GPS-denied navigation capability."
- "Every week, there’s an article about a commercial flight or defense-related mission getting GPS jammed, like thousands of flights to and from Europe affected by suspected Russian jamming," said investor Tony Fadell. ANELLO has worked with the US Department of Defense on optimising its algorithms against jamming or spoofing.
- The post ANELLO Photonics secures funding for inertial navigation in GPS-denied environments appeared first on The Robot Report.
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Unite
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Enfabrica Secures $115M Series C Funding and Announces Availability of World’s Fastest GPU Networking Chip
- Enfabrica Corporation has raised $115m in Series C funding, led by Spark Capital, to take artificial intelligence (AI) infrastructure to the next level.
- In addition, Enfabrica announced the launch of the Accelerated Compute Fabric (ACF) SuperNIC chip, potentially the industry's first networking card that can process 3.2 terabits per second.
- Enfabrica plans to use the funds to ramp up production of the chip and expand its R&D capability.
- The ACF SuperNIC provides a software-defined networking system, giving data centre operators full-stack control and programmability over their infrastructure.
- Enfabrica's investors include Atreides Management, Alumni Ventures, Liberty Global Ventures, Sutter Hill Ventures, and Valor Equity Partners.
- The chip's design allows data centres to scale up to 500,000 graphic processing units while maintaining low latency and optimised performance.
- It provides an 800-Gigabit Ethernet connectivity for multi-port and multipath resiliency, establishing Enfabrica as a leading innovator in scalable AI networking solutions.
- Enfabrica's funding comes during a period of rapid investor interest, following a successful Series B funding round of $125m in September 2023.
- Enfabrica's ACF SuperNIC chip is available in Q1 of 2025 and will partner with OEM and ODM systems for full-scale commercial availability later that year.
- Through this breakthrough technology, Enfabrica is anticipating improving the efficiency, resiliency and scalability of AI clusters globally.
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The Robot Report
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Nuro Driver expands Level 4 autonomous deliveries in California and Texas
- Nuro Inc. expands its driverless capabilities using zero-occupant vehicles with the AI-powered Nuro Driver system.
- The expansion covers multiple cities in California and Texas and includes operational advancements.
- Nuro plans to increase deployment areas in Mountain View, Palo Alto, and Houston.
- Nuro's expanded operational design domain demonstrates the growing sophistication and reliability of its autonomous vehicles.
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Arduino
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Exploring fungal intelligence with biohybrid robots powered by Arduino
- Cornell University researchers have created biohybrid robots that are powered by fungal mycelia.
- The robots move based on electrical signals generated by fungi, expanding the possibilities in the field of biohybrid robotics.
- The team used Arduino platform as the main interface to control the robots, enabling seamless prototyping and experimentation.
- This research opens up potential applications in various fields, from environmental sensing to pollution monitoring.
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The Robot Report
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The ABB Edge: Using AI and robotics to transform industries
- ABB is using AI and robotics to transform industries, making robotics easier to program and deploy.
- Schaeffler plans to use Agility Robotics' humanoid robot Digit in its global operations.
- Waymo expands its robotaxi service in Los Angeles, removing the waitlist and gaining valuable feedback from rides.
- General Motors' self-driving car unit, Cruise, will pay a $500k fine for submitting a false report.
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