Open Source
Assembly Line
Adopting open-source Industrial IoT software
Siloed solutions and ad-hoc efforts to tap into the fourth industrial revolution by funding one-time AI/ML and digitalisation projects in manufacturing fell short of their promises. Enterprises did not address the fundamental challenges behind the lagging security, updates and maintenance in industrial hardware, but only focused on applying the latest technologies. Legacy install bases and a lack of standardisation prevented industrial transformation from occurring. To fully reap the benefits of Industry 4.0, the industrial factory has to close the gaps between Operational Technology (OT) and IT. The convergence between the two domains calls for a transition from legacy stacks with closed standards and interfaces to modern IT solutions and the embrace of open-source software.
Building openness into industrial robotics
Since we began working together, the Open Robotics team at Intrinsic has first and foremost been continuing its efforts as core contributors to improve ROS, Gazebo, and Open-RMF as tools and resources that serve as backbones for robotics projects the world over. In the past months, we’ve celebrated Iron Irwini, the latest ROS release, and Harmonic, the latest Gazebo release. Today at ROSCon, the Open Robotics team at Intrinsic introduced the progress we’re making on the development of alternative middleware protocols as well as our first integrations between ROS and the Intrinsic platform.
In addition to core contributions to ROS, the Open Robotics team at Intrinsic is also working on building the first integrations between ROS and the Intrinsic platform. When we announced Intrinsic Flowstate earlier this year as our first web-based developer tool to make building robotic solutions easier and more accessible, we indicated our intention to build strong bridges between ROS and our Intrinsic platforms. With Flowstate currently in private beta, we’re gradually increasing the number of testers and exploring prototypes of potential integrations between our platforms.
Intel's Open Source Strategy
Well, I mean, Pat always says, “Software defined, hardware enabled.” So, you can build the finest piece of hardware, but if the software is not going to run on it it’s not going to be very helpful, right? And that’s honestly the reasons that we contribute to open source all along, and we have been contributing for over two decades. Because our customers they consume our product, which is a silicon using these open-source projects. So, you pick a project OpenJDK, PyTorch, TensorFlow, scikit-learn, Kafka, Cassandra, Kubernetes, Linux kernel, GCC. And our customers who want to consume our silicon they want to make sure that these open-source projects are consumed well on the Intel silicon, they behave well, and they are able to leverage all the features that are in the instruction set of the latest edition of the chip.
GM Ramps Up Effort to Bring Customers Seamless Software Experiences Through Unified Automotive Standards
General Motors announced today that it is joining the Eclipse Foundation, one of the world’s largest open source software foundations. It has also contributed “uProtocol” as a starting point for greater standardization, enabling increased software productivity across the automotive industry that can lead to easy-to-use, software-enabled customer experiences. This protocol aims to speed up software development by streamlining the creation of software that is distributed across multiple devices within vehicles as well as across the cloud and mobile.
GM will participate in the Eclipse Foundation’s Software Defined Vehicle (SDV) Working Group, which is focused on accelerating innovation of automotive-grade software stacks using open source and open specifications developed by and for a growing community of engineers and member companies. Collaborators on GM’s Ultifi software platform including Microsoft and Red Hat, as well as multiple other automakers, participate in the group.
🦾 Amazon releases largest dataset for training 'pick and place' robots
In an effort to improve the performance of robots that pick, sort, and pack products in warehouses, Amazon has publicly released the largest dataset of images captured in an industrial product-sorting setting. Where the largest previous dataset of industrial images featured on the order of 100 objects, the Amazon dataset, called ARMBench, features more than 190,000 objects. As such, it could be used to train “pick and place” robots that are better able to generalize to new products and contexts.
The scenario in which the ARMBench images were collected involves a robotic arm that must retrieve a single item from a bin full of items and transfer it to a tray on a conveyor belt. The variety of objects and their configurations and interactions in the context of the robotic system make this a uniquely challenging task.
The state of open-source in 3D printing in 2023
The above and many other things we’ve been doing at Prusa Research for over ten years were only possible thanks to the great 3D printing community and open-source philosophy. However, the new printers and software releases have made me think again about the current state of open source in the 3D printing world. How sustainable it is, how our competitors deal with it, what it brings to the community, and what troubles us as developers. Consider this article as a call for discussion – as a kick-off that will (hopefully) open up a new perspective on the connection between open-source licensing, consumer hardware, and software development.
How Git-Based Source Control Drives IT/OT Convergence
The topic of robust data management is often overlooked in the convergence conversation; however, it is an area of IT expertise that can be easily applied to OT processes, yielding huge benefits. Git-based source control coupled with formalized review practices, a staple in traditional software development, represents an opportunity unmatched in driving OT team productivity and increased code quality.
Using Git repositories and processes as a framework for OT source control can align IT and OT. From setup, participating IT team members gain immediate visibility into crucial OT systems, their file structures, and the processes used to develop control programs. Likewise, OT teams realize the benefits of securing and tracking code changes, unlocking easy review workflows, and quick code recovery during incidents.
Git-based version control is not common in industrial automation environments. The backbone of OT networks are the PLC control systems that drive manufacturing machinery. PLC systems are often written in visual languages (i.e., ladder logic and function block diagrams) using proprietary development tools. The result is a collection of local binary files on an engineer’s desktop or control devices.
Recently Copia Automation has developed new tools to unlock Git’s full power for these file formats. When using Copia, automation professionals can track all changes, visualize the file outside the development environment, and see the highlighted differences between the versions. Add in the power of Git branching and merging, and Copia delivers a source control framework that enables engineers to build code together, collaborate more effectively, and review all program changes quickly and thoroughly.
RT-1: Robotics Transformer for Real-World Control at Scale
Major recent advances in multiple subfields of machine learning (ML) research, such as computer vision and natural language processing, have been enabled by a shared common approach that leverages large, diverse datasets and expressive models that can absorb all of the data effectively. Although there have been various attempts to apply this approach to robotics, robots have not yet leveraged highly-capable models as well as other subfields.
Several factors contribute to this challenge. First, there’s the lack of large-scale and diverse robotic data, which limits a model’s ability to absorb a broad set of robotic experiences. Data collection is particularly expensive and challenging for robotics because dataset curation requires engineering-heavy autonomous operation, or demonstrations collected using human teleoperations. A second factor is the lack of expressive, scalable, and fast-enough-for-real-time-inference models that can learn from such datasets and generalize effectively.
To address these challenges, we propose the Robotics Transformer 1 (RT-1), a multi-task model that tokenizes robot inputs and outputs actions (e.g., camera images, task instructions, and motor commands) to enable efficient inference at runtime, which makes real-time control feasible. This model is trained on a large-scale, real-world robotics dataset of 130k episodes that cover 700+ tasks, collected using a fleet of 13 robots from Everyday Robots (EDR) over 17 months. We demonstrate that RT-1 can exhibit significantly improved zero-shot generalization to new tasks, environments and objects compared to prior techniques. Moreover, we carefully evaluate and ablate many of the design choices in the model and training set, analyzing the effects of tokenization, action representation, and dataset composition. Finally, we’re open-sourcing the RT-1 code, and hope it will provide a valuable resource for future research on scaling up robot learning.
How Boeing Uses Cloud Native
“Being able to leverage the best technologists out there in the rest of the world is of great value to us strategically,” Torres, chief engineer of open source and cloud native for Boeing, said. This strategy allows Boeing to “differentiate on what we do as our core business rather than having to reinvent the wheel all the time on all of the technology.”
Like many other large companies, Boeing has created an open source office to better work with the open source community. Although Boeing is primarily a consumer of open source software, it still wants to work with the community. “We want to make sure that we have a strategy around how we contribute back to the open source community, and then leverage those learnings for inner sourcing,” he said.