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Robotics News
The Robot Report
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RBR50 Spotlight: Robust AI deploying Carter AMR with DHL
- DHL Supply Chain partners with Robust AI to deploy the Carter AMR.
- Carter AMR is a human-centric robot designed for dynamic warehouse environments.
- The partnership with DHL validates Robust AI's technology and opens doors for growth.
- Robust AI's Carter AMR will be deployed later in 2024 for optimizing picking processes.
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Analyticsindiamag
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NVIDIA Isaac Lab 1.2 Generally Available on GitHub, New Robot Learning Tools Out
- NVIDIA has announced the general availability of NVIDIA Isaac Lab 1.2 on GitHub, offering advanced tools and workflows for robotics development.
- The release includes the NVIDIA Isaac Lab framework, specialized workflows for humanoid robot development under Project GR00T, and new video data processing tools.
- Isaac Lab supports training robots at scale, while Project GR00T introduces workflows for addressing core challenges in humanoid robotics.
- NVIDIA's leadership in robotics was further demonstrated through research papers and workshops presented at the Conference for Robot Learning.
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Medium
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Robotics Programming for Beginners in Dubai
- Before diving into complex robotics projects, it’s essential to understand some basic concepts.
- Dubai offers a wealth of options for beginners to learn robotics, from local seminars to online courses.
- There are beginner-friendly robotics kits available online and in Dubai, making it easy to master the fundamentals.
- Start with simpler tasks and gradually progress to more difficult ones, persevering through challenges while programming robotics.
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Medium
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Nephele — Redefining Education and Engagement with AI and Robotics
- The AWS Cloud Club and PEP - Cloud Computing Centre at St. Joseph's Institute of Technology have created Nephele, an intelligent robot designed to revolutionize classroom engagement and event management.
- Nephele 1.0, the initial prototype, showcased the potential of cloud-driven robotics in education with features like face recognition, command-following, and AI integration.
- Building Nephele 1.0 posed challenges, such as integrating cloud-based AI into a mobile platform, but provided valuable learning experiences for the team.
- The success of Nephele 1.0 has inspired students to explore robotics, cloud computing, and AI fields, and the team is already working on Nephele 2.0 with enhanced features and deeper integration of AI and cloud services.
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The Robot Report
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Stanford Robotics Center partners with Stanford HAI for AI research, policy
- The Stanford Robotics Center and the Stanford Institute for Human-Centered Artificial Intelligence have formed a partnership to explore responsible uses of AI and accelerate the field of robotics.
- The partnership aims to address technical, societal, and economic challenges in areas like health, education, sustainability, and the future of work.
- The organizations will translate multidisciplinary research into AI-enabled robotics for policymakers and produce original research to aid in understanding and governing the technology.
- The collaboration seeks to ensure the safe and successful adoption of robotics technology by working together to understand its potential and dangers.
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The Robot Report
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Formant partners with Holman on cloud robotics platform
- Formant, a developer of a leading cloud robotics platform, has partnered with Holman, a global automotive services company.
- This collaboration will combine Formant’s cloud robotics platform with Holman’s fleet and automotive expertise.
- The partnership aims to transform how businesses manage their fleet while integrating physical automation.
- Formant and Holman will innovate and provide cutting-edge solutions in the evolving robotics and fleet management industry.
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The Robot Report
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CynLr raises Series A funding to realize robot vision for ‘universal factory’
- CynLr has raised $10m in series A funding to develop its ability to make robots automatically recognize and manipulate objects and become true factories in a “factory-as-a-product” concept.
- The US start-up hopes to improve performance, reliability and user experience with the funding, allocate more funds to research and development (R&D), reduce costs for customers and expand its team.
- CynLr’s technology aims to allow robots to manipulate objects of any shape, colour, size and form toward the aim of developing a “universal factory”.
- The company plans to use the R&D funds to gain deeper understanding of managing demand variability for different robot parts via a hot-swappable robot station, and working with customers like General Motors to agree on one standard robot platform that can manage 22,000+ parts for assembly.
- It is also working with customers in the US and EU to produce pilot applications of the factory-as-a-product vision and viewing the Series A investment as critical in bringing its technology to the masses.
- CynLr claims its robot vision stack, inspired by human eyesight, and its Event Imaging technology, can be used on any object without requiring human training on the robot’s movements.
- CyRo, the three-armed robot developed by CynLr, can comprehend, grasp and manipulate objects in complex environments with ease thanks to its innovative robotic vision stack inspired by the human eye.
- The Series A funds came from Pavestone Capital, Grow X Ventures, and Athera Ventures, with existing investors Infoedge, Speciale Invest and others also contributing.
- CynLr now plans to develop its 60-member core team into a 120-member international team that will include business leaders, marketing and sales teams and employees across India, the US and Switzerland.
- It also plans to open up a “Cybernetics H.I.V.E.” that will double in size to 50 robots by 2026.
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The Robot Report
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iRobot lays off another 105 employees
- iRobot, the maker of Roomba robot vacuum, is laying off 105 employees.
- The layoffs represent 16% of iRobot's global workforce.
- The company has faced challenges due to the failed acquisition by Amazon, competition, and the failure to innovate beyond robot vacuums.
- iRobot's CEO mentioned that the restructuring has resulted in reduced operating expenses and improved financial performance.
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Unite
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Birago Jones, Co-Founder and CEO of Pienso – Interview Series
- Birago Jones is the CEO and Co-Founder of Pienso, a no-code/low-code platform for enterprises to train and deploy AI models without the need for advanced data science or programming skills.
- Pienso's interactive learning interface is designed to enable users to harness AI to its fullest potential without any coding.
- Pienso eliminates the barriers of “MLOps” — data cleaning, data labeling, model training and deployment.
- Pienso makes it possible to train models directly on an organization's own data, alleviating the privacy concerns of using foundational models, and can also deliver more accurate insights.
- Pienso also integrates with existing enterprise systems through APIs, allowing inference results to be delivered in different formats.
- As the demand for high-volume text analytics grows, Pienso will enhance its ability to handle larger datasets with faster inference times and more complex analysis.
- Pienso will push further into democratizing AI by refining the interface and offering more tailored solutions for diverse industries.
- Pienso will work alongside the solutions/tools from the major cloud providers and on-premise solutions, building stronger integrations with other data platforms and tools.
- Birago Jones is a distinguished authority in the intersection of artificial intelligence (AI) and human-computer interaction (HCI), and an advocate for responsible AI.
- Pienso's customers include the US government and Sky, the largest broadcaster in the UK.
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Unite
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Microsoft AutoGen: Multi-Agent AI Workflows with Advanced Automation
- Microsoft Research introduced AutoGen as an open-source Python framework for building AI agents capable of complex, multi-agent collaboration.
- AutoGen Studio is a low-code interface, which simplifies the development of AI agents by providing a graphical user interface (GUI) to configure workflows.
- AutoGen is a multi-agent framework that supports various agent types and conversation patterns, automation of workflows for diverse industries such as finance, advertising, and software engineering.
- AutoGen introduces conversable agents that can engage in rich dialogues and can be customized to improve their performance, making it ideal for both simple and complex AI projects.
- AutoGen’s most impressive feature is its support for multi-agent collaboration, which provides developers the flexibility to create a network of agents to tackle complex tasks efficiently.
- AutoGen allows agents to generate, execute, and debug code automatically. It helps minimize human intervention and speeds up development cycles.
- AutoGen supports interaction with external tools, services, and APIs, effectively expanding the capabilities of agents to perform diverse range of applications.
- AutoGen’s strength lies in its ability to coordinate multiple agents to complete tasks that are interdependent, thus helping in building complex AI systems.
- AutoGen is an open-source, python-based framework that requires a solid understanding of AI agents, orchestration frameworks, and the basics of Python programming. It is also a powerful tool for cloud-native strategies.
- Possible challenges that might come while using AutoGen are security, scalability, and ethical concerns.
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Unite
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3 Data-Proven Ways Companies Can Increase AI Adoption and Boost Productivity
- AI adoption plays a crucial role in AI-driven productivity
- Employees fall into three distinct categories when it comes to AI adoption.
- A flexible, data-driven approach to AI adoption yields greater benefits.
- Effective communication procedures between managers and employees, especially AI tools adoption, are crucial.
- Training and established usage guidelines are essential to increase AI adoption.
- AI-powered large language models can drive business decisions in real-time.
- It’s critical to strike a balance between relying on data and leveraging the expertise of experienced staff and senior leadership.
- More employees must embrace AI tools to leverage its full benefits and improve productivity.
- The impact of AI is substantial, but it requires constant refinement, communication, and training to maximize its potential.
- The most successful organizations will be those that stay flexible, monitor AI usage trends closely, and make data-driven decisions.
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Nvidia
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NVIDIA Advances Robot Learning and Humanoid Development With New AI and Simulation Tools
- NVIDIA has launched a new set of AI and simulation tools at the Conference for Robot Learning in Munich.
- The tools will speed up work on AI-based robots, especially humanoids.
- Among the tools is the NVIDIA Isaac Lab robot learning framework.
- NVIDIA also announced the release of six new humanoid robot learning workflows for the Project GR00T initiative.
- The platform will provide accelerated libraries, foundation models, and data pipelines to speed the development of humanoid robots.
- NVIDIA has also launched new modelling tools, including the Cosmos tokenizer and NeMo Curator for video processing.
- The Cosmos tokenizer provides robotics developers superior visual tokenization by breaking down images and videos into high-quality tokens with exceptionally high compression rates.
- NVIDIA presented 23 papers and nine workshops related to robot learning and released training and workflow guides for developers.
- Hugging Face and NVIDIA announced they’re collaborating to accelerate open-source robotics research with LeRobot, NVIDIA Isaac Lab and NVIDIA Jetson for the developer community.
- The humanoid robot framework applies to any embodiment, including humanoids, to handle increasingly complex movements and interactions.
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Nvidia
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Hugging Face and NVIDIA to Accelerate Open-Source AI Robotics Research and Development
- Hugging Face and NVIDIA have announced a collaboration to accelerate robotics research and development.
- The collaboration aims to bring together their open-source robotics communities to drive advances in various industries.
- Hugging Face's LeRobot platform and NVIDIA's AI and robotics technology will enable researchers and developers to access open-source frameworks for robot training, simulation, and inference.
- The collaboration will streamline the development process and create a data flywheel for the robotics community.
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VentureBeat
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Nvidia advances robot learning and humanoid development with AI and simulation tools
- Nvidia has announced new AI and simulation tools that can push the advancement of humanoid development and robot learning. The tools will allow developers to significantly boost their work on AI-enabled robots, helping them to accelerate robot and humanoid development. Nvidia has also revealed six new humanoid robot learning workflows for Project GR00T and a new Nvidia Isaac Lab robot learning framework.
- Nvidia Isaac Lab is built using the open-source robot learning framework, Nvidia Omniverse. At scale, developers can use the framework to train robot polices. The software applies to any embodiment robot, including humanoids to quadrupeds and collaborative robots.
- Isaac Lab is set to be used by many top robot makers, including Agility Robotics, Field AI and Swiss-Mile, among others.
- Nvidia has also released 23 papers for the advancement of robot learning, including nine workshops at the Conference for Robot Learning (CoRL) in Germany.
- World models, simulation, robotics application development, and world curation pose significant challenges due to their data-intensive and computational necessity. Nvidia has, therefore, released the Nvidia Cosmos tokenizer and the Nvidia NeMo Curator for video processing to facilitate high compression rates and curation without compromising visual tokenization.
- With humanoid robots, building advanced humanoids is regarded as a challenging multilayered task that demands a collaborative and technological approach. Building such humanoids require robots that can perceive, act and learn human-robot interactions effectively.
- Nvidia’s Project GR00T initiative aims to foster the accelerated development of humanoid robot ecosystems by providing accelerated libraries, foundation models, and data pipelines. The workflows for Project GR00T enable humanoid developers to realise the most challenging humanoid robot capabilities.
- 1X, a humanoid robot company, has updated the 1X World Model Challenge dataset to integrate with Nvidia Cosmos tokenizer. Other developers like Xpeng Robotics and Hillbot are using the tool to maintain high-resolution videos and images.
- The Nvidia Cosmos tokenizer is available on Hugging Face and GitHub, while the Nvidia Isaac Lab is open source on GitHub. NeMo Curator will be available later this month.
- Nvidia aims to reduce processing time for AI development and promote scalability across multi-node GPU systems, hence reducing costs for Robot developers and AI curators.
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