Integrating Robotics with Machine Learning Technologies

VLA models are systems that combine three capabilities into one framework: seeing the world through cameras, understanding natural language instructions like "pick up the red apple," and generating the actual motor commands to make a robot do it. Before these unified models existed, robots had separate modules for vision, language, and movement that were stitched together with manual engineering, which made them brittle and unable to handle new situations. This review paper covers over 80 VLA models published in the past three years, organizing them into a taxonomy based on their architectures—some use a single end-to-end network, others separate high-level planning from low-level control, some use diffusion models for smoother action sequences. The paper walks through how these models are trained using both internet data and robot demonstration datasets, then maps out where they're being applied. The later sections lay out the concrete technical problems that remain unsolved. Read online with an AI tutor: https://lnkd.in/eZdzYfdu PDF: https://lnkd.in/ezzncewE

#SoftRobotics is a rapidly evolving field where #FiniteElementAnalysis (#FEA), #MachineLearning (ML), and #DigitalTwins (DT) are revolutionizing the design and functionality of these adaptive and intelligence systems. In our recent review accepted for publication in Smart Materials and Structures (IOP Publishing), we explore: ✅ How FEA and ML are transforming soft robotics: from guiding material selection and structural design to optimizing #sensing, #control, and #actuation. ✅ The pivotal role of Digital Twins: enabling real-time monitoring, predictive maintenance, and remote operation for soft robotic systems. ✅ #4Dprinting innovations: unlocking dynamic capabilities through #shapememory properties, enabling soft robots to recover their original forms under thermal stimuli. This extends the lifespan of robotic systems and supports sustainable manufacturing practices. ✅ Future outlook: highlighting challenges like material modeling and system integration, while emphasizing the combined potential of FEA, ML, and DT to expand the applications of soft robots in industries like healthcare, aerospace, exploration, and more. 📖 Please read the full paper with over 800 references here: https://lnkd.in/ey3U6gfk and shear your thoughts Research Team: Liuchao Jin (Leading Author), Xiaoya Zhai, Wenbo Xue, Kang Zhang, Jingchao Jiang, Mahdi Bodaghi, Wei-Hsin Liao (Team Leader).

Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language, and Action for Advanced Robotics, UI Navigation, and Intelligent Decision-Making Researchers from Microsoft Research, the University of Maryland, the University of Wisconsin-Madison KAIST, and the University of Washington introduced Magma, a foundation model designed to unify multimodal understanding with action execution, enabling AI agents to function seamlessly in digital and physical environments. Magma is designed to overcome the shortcomings of existing VLA models by incorporating a robust training methodology that integrates multimodal understanding, action grounding, and planning. Magma is trained using a diverse dataset comprising 39 million samples, including images, videos, and robotic action trajectories. It incorporates two novel techniques, Magma employs a combination of deep learning architectures and large-scale pretraining to optimize its performance across multiple domains. The model uses a ConvNeXt-XXL vision backbone to process images and videos, while an LLaMA-3-8B language model handles textual inputs. This architecture enables Magma to integrate vision-language understanding with action execution seamlessly. It is trained on a curated dataset that includes UI navigation tasks from SeeClick and Vision2UI, robotic manipulation datasets from Open-X-Embodiment, and instructional videos from sources like Ego4D, Something-Something V2, and Epic-Kitchen. By leveraging SoM and ToM, Magma can effectively learn action grounding from UI screenshots and robotics data while enhancing its ability to predict future actions based on observed visual sequences. During training, the model processes up to 2.7 million UI screenshots, 970,000 robotic trajectories, and over 25 million video samples to ensure robust multimodal learning..... Read full article: https://lnkd.in/gc9xuEWH Paper: https://lnkd.in/gB8PHj9y Project Page: https://lnkd.in/gJYvjYZg Microsoft Microsoft Research

Robotics is entering a new phase where learning is becoming more autonomous, scalable, and efficient. Instead of relying heavily on large volumes of human-labeled training data, emerging approaches allow robots to learn through simulation, self-exploration, and real-time adaptation. This shift has the potential to significantly reduce development time while improving flexibility across dynamic environments. In practical terms, this means robots can better understand how to interact with unfamiliar objects, refine their movements through trial and feedback, and generalize skills across tasks without being explicitly programmed for each scenario. From manufacturing floors to logistics and even healthcare support, the impact could be substantial. While the progress is promising, it also brings important considerations around reliability, safety, and oversight. As robots gain more independence in how they learn and act, ensuring robust validation and responsible deployment becomes critical. The evolution from data-dependent training to self-directed learning is not just a technical milestone. It represents a broader shift toward more adaptive and intelligent systems that can collaborate with humans more effectively and operate in increasingly complex real-world settings.

I’m thrilled to share my new article on an exciting technological fusion of ROS (Robot Operating System) and ChatGPT—ROSGPT! In this piece, I dive into how AI-driven natural language processing can seamlessly connect with robotic hardware and software for a more intuitive, human-friendly robotics experience. In my article, you will learn: - How ROSGPT bridges the gap between robotic actions and natural language commands. - Practical examples and step-by-step instructions to get started. - The potential impact of AI integration on robotics research and development. If you’re curious about leveraging artificial intelligence to enhance robotic capabilities, or you want to explore cutting-edge developments in robotics, this article is for you. Check it out here and let me know your thoughts. Let’s shape the future of robotics together! Dr. Ivan Del Valle Head of Apsley Labs and Global Artificial Intelligence and Emerging Technologies Director Apsley Business School, London Sebastian Fuller DOW Academic Campus #ROSGPT #ArtificialIntelligence #RoboticsInnovation #AIinRobotics #OpenAI #ROS

Figure AI, with the support of Microsoft and OpenAI, is integrating advanced humanoid robots into BMW’s production processes, beginning in Spartanburg, South Carolina. This collaboration signifies a paradigm shift towards human-like automation in manufacturing, leveraging AI to enhance precision and efficiency. Technically, these robots stand out for their use of machine learning, computer vision, and natural language processing, enabling them to perform complex tasks and interact with human workers in dynamic production environments. Figure's humanoid robots enable the automation of difficult, unsafe, or tedious tasks throughout the manufacturing process, which in turn allows employees to focus on skills and processes that cannot be automated, Brett Adcock, Founder and CEO of Figure, emphasized the untapped potential of general-purpose robotics in revolutionizing productivity, reducing operational costs, and fostering a safer, more consistent working environment. Do you believe our society is ready for humanoid robots? #trends #ai #manufacturing

Google DeepMind has introduced new AI models, 𝐆𝐞𝐦𝐢𝐧𝐢 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐆𝐞𝐦𝐢𝐧𝐢 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬-𝐄𝐑, aimed at improving robots’ ability to adapt to complex real-world environments. These models leverage large language models to enhance reasoning and dexterity, enabling robots to perform tasks such as folding origami, organizing desks, and even playing basketball. The company is collaborating with start-up Apptronik to develop 𝐡𝐮𝐦𝐚𝐧𝐨𝐢𝐝 𝐫𝐨𝐛𝐨𝐭𝐬 using this technology. The advancements come amid competition from Tesla, OpenAI, and others to create AI-powered robotics that could revolutionize industries like manufacturing and healthcare. 𝐍𝐯𝐢𝐝𝐢𝐚’𝐬 𝐂𝐄𝐎, 𝐉𝐞𝐧𝐬𝐞𝐧 𝐇𝐮𝐚𝐧𝐠, 𝐡𝐚𝐬 𝐜𝐚𝐥𝐥𝐞𝐝 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐫𝐨𝐛𝐨𝐭𝐢𝐜𝐬 𝐚 𝐦𝐮𝐥𝐭𝐢𝐭𝐫𝐢𝐥𝐥𝐢𝐨𝐧-𝐝𝐨𝐥𝐥𝐚𝐫 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲. Unlike traditional robots that require manual coding for each action, Gemini Robotics allows robots to adjust to new environments, follow verbal instructions, and manipulate objects more effectively. The AI runs in the cloud, leveraging Google’s vast computational resources. Experts praise the development but note that general-purpose robots are still not ready for widespread adoption. 𝐑𝐞𝐚𝐝 𝐌𝐨𝐫𝐞: https://lnkd.in/gd4gAtFp

Deep learning curriculum for robotics. [📍 Link to Playlist below ] Modern robotics workflows often integrate deep learning models with traditional algorithms for mapping, localization, and control. This playlist builds it from scratch: Single neurons → Backprop → CNNs → Transformers → GANs → AlphaGo. Step by step. Taught by Prof. Bryce, one of the clearest educators in the field. 26 lectures covering: → CNNs (robotic vision & object detection) → Transformers (planning & foundation models)→ LSTMs/RNNs (sequential decision-making) → GANs (synthetic training data) → AlphaGo (reinforcement learning for robot control) If you’re building robots or trying to understand how they learn, this is the foundation. 📍 https://lnkd.in/eKmTGBwQ

Most people agree that learned approaches are here to stay in robotics. That’s the easy part. Over the past few years, we’ve seen learning-based methods like imitation learning, reinforcement learning, diffusion policies gain serious traction. It is clear that there is more coming. But the real challenge isn’t just technical novelty. It’s figuring out the transition strategy while these approaches mature. If a company already has a reliable MPC controller deployed in the field, they can’t just throw it away overnight and replace it with a learned policy. There are customers to serve, safety constraints to meet, and months (if not years) of edge-case handling baked into the stack. So we say: “Let’s combine both.” Sounds great. But what does that actually look like? One common approach is to use the existing controller to generate rollouts and train a policy to mimic and then robustify those behaviors. Again, sounds easy. But in practice, you’re left facing harder, more practical questions: How much data is enough? Over what distribution of tasks? What kind of model can encode multiple distinct behaviors robustly? This is where hype meets reality. In a hype cycle, humanoids are already out in the factory and we are there. In the real world, every one of these questions is very hard to answer. And the answers don’t live in a vacuum, but they depend on the system, the task, the risk tolerance, and the operational constraints. This isn’t to say it can’t be done. It can. But we need more conversations about transitions, not just breakthroughs. That’s where the real impact lies. #Robotics #MachineLearning #ImitationLearning #ReinforcementLearning #RoboticsResearch #MPC #ControlSystems #RobotLearning #RealWorldAI #TechTransition #Sim2Real #AutonomyStack

Special thanks to Prof. Ahmed H. Qureshi for the personalized tour of his CORAL Lab at Purdue University. CORAL’s research spans machine learning, robot planning and control, and my personal favorite area, safe human–robot collaboration. Together, these threads tackle one of the hardest problems in robotics today: how autonomous systems can learn, plan, and act effectively while operating alongside people in real, unstructured environments. What stood out to me is how the lab integrates learning and decision-making with explicit attention to safety, interaction, and shared spaces. CORAL explores how robots can reason about uncertainty, model human behavior, and adapt their plans in ways that support collaboration rather than conflict. This includes work on risk-aware planning, learning-based control, and interaction-aware decision-making that directly addresses how robots should behave around and with humans. Several of my favorite papers from the lab dive deeply into these themes, including work on safe and interactive planning, human-aware risk representations, and learning frameworks that support trustworthy collaboration between humans and robots. I’ve shared links to a few of these papers below for anyone who wants to explore further. As robots increasingly leave controlled environments and enter factories, hospitals, warehouses, and public spaces, this kind of research becomes foundational. Autonomy that ignores the human context will struggle to scale. Autonomy that understands and respects it has the potential to truly transform how we work and live. Many thanks again to Ahmed and the CORAL team for the warm welcome and the great conversations (remember: replace the banana with a beer bottle for social impact! 😉 ). It was energizing to see research that so clearly connects theory, algorithms, and real-world human impact. Purdue Computer Science ---- For those interested in going deeper, here are a few of my favorite papers from the CORAL Lab that really capture the breadth and impact of their work: 🔹 Safe and interactive planning for human–robot collaboration https://lnkd.in/eMMqED3r https://lnkd.in/eQJtSinY 🔹 Risk-aware representations and decision-making around humans https://lnkd.in/ep4dRPHM 🔹 Learning and control frameworks that enable safe, trustworthy interaction https://lnkd.in/eVjcQBpa https://lnkd.in/eSH-_CrV These papers do a great job of connecting learning, planning, and control with the realities of shared human–robot environments. Highly recommend a read if you’re working in robotics, autonomy, or human–robot interaction.