Adriana Miraglia’s Post

A technique for more effective multipurpose robots: MIT researchers developed a technique to combine robotics training data across domains, modalities, and tasks using generative AI models. They create a combined strategy from several different datasets that enables a robot to learn to perform new tasks in unseen environments. #ArtificialIntelligence #MachineLearning #DataScience

With generative AI models, researchers combined robotics data from different sources to help robots learn better. | Click below to read the full article on Sunalei

The fusion of robotics and artificial intelligence is reshaping our technological landscape. A recent Quanta Magazine podcast raises a crucial question: "How can we seamlessly integrate the physical capabilities of robots with the cognitive power of AI?" As these technologies converge, the potential for innovation in education is limitless. By supporting and advancing robotics education, we can equip the next generation with the skills and knowledge they need to thrive in this rapidly evolving field. https://lnkd.in/e53DiPku #Robotics #AI #Innovation #STEMEducation

MIT's latest research on generative AI pushes us closer to general-purpose humanoids. Robots can now perform multiple tasks with better precision and adaptability by combining diffusion models and innovative training methods. Imagine robots swapping tools and seamlessly handling new challenges! 🌟 #TechInnovation #GenerativeAI #RoboticsRevolution

🚀 Generative AI is taking robots a step closer to becoming general-purpose humanoids! While much of the spotlight has been on the hardware, the real game-changer lies in AI. Recent research from MIT reveals that generative AI, particularly through a method called policy composition (PoCo), can significantly enhance robot capabilities. By combining task-specific strategies into a unified policy, robots can now perform multiple tasks with improved dexterity and adaptability. This approach has shown a 20% improvement in task performance, bringing us closer to the dream of versatile, general-purpose robots. The future of robotics is here, and it's smarter than ever! 🤖✨ #AI #Robotics #Innovation #MITResearch #FutureTech https://lnkd.in/e6T4RA3s Brian Heater TechCrunch

Generative AI takes robots a step closer to general purpose Experience the next leap in robotics as generative AI paves the way for general-purpose functionality. Find out how algorithms can transform robots. #ai #technews #robotics #innovation Read More: https://shorturl.at/UA5HB

One of the biggest challenges of developing Heterogeneous Pretrained Transformers (HPT) was building the massive dataset to pretrain the transformer, which included 52 datasets with more than 200,000 robot trajectories in four categories, including human demo videos and simulation. What do you think? #data #robotics #ai #machinelearning #llm #engineering https://lnkd.in/eTy7Ui5d

How AI is Revolutionizing Robotics: My Perspective 🚀 The robotics field is evolving at an unprecedented pace, and artificial intelligence (AI) is undoubtedly the catalyst driving this transformation. When I look back at the early days of robotics, much of the work was deterministic—hard-coded instructions, predefined paths, and limited adaptability. Fast forward to today, and AI has unlocked new possibilities that we could only dream of a decade ago. Here are three key ways I believe AI is revolutionizing robotics: 1️⃣ Enhanced Perception and Understanding AI-powered perception systems enable robots to not just "see" but "understand" their environments. With computer vision and deep learning, robots can recognize objects, navigate dynamic environments, and even interpret human gestures. Example: Autonomous drones using AI for obstacle avoidance can now react to unpredictable changes mid-flight, making applications like search-and-rescue missions safer and more reliable. 2️⃣ Smarter Decision-Making Gone are the days of rigid, preprogrammed logic. AI allows robots to make decisions based on real-time data, adapting to uncertainties. Reinforcement learning and probabilistic reasoning empower robots to explore, learn, and optimize their tasks dynamically. Example: In agriculture, robots equipped with AI can analyze soil conditions, identify weeds, and adjust their actions to maximize crop yield—a level of precision that was impossible before. 3️⃣ Collaborative and Human-Centric Robots With AI, robots are becoming better collaborators, seamlessly working alongside humans in manufacturing, healthcare, and even our homes. Natural language processing and predictive analytics ensure that robots understand human intent and anticipate needs. Example: Cobots (collaborative robots) in factories are leveraging AI to predict failures and assist workers in tasks, boosting both efficiency and safety. Final Thoughts 💡 AI and robotics are no longer two separate disciplines; they’re converging to create machines that can learn, reason, and evolve. But with great power comes great responsibility. As we integrate AI into robotics, we must also consider ethical challenges—bias in AI models, data privacy, and the implications of replacing human labor. I believe that the future of robotics lies in striking a balance: creating AI-driven systems that augment human capabilities while fostering trust and accountability. 🌟 Over to you! What’s one area where you think AI is having the biggest impact on robotics? Let me know your thoughts in the comments! #RoboticsEngineering #AI #ArtificialIntelligence #Automation #FutureOfWork

New AI models combine understanding of language and vision with data from robotic sensors and actuators. This makes it possible for humans to interact with robots using ordinary words https://lnkd.in/eVRfyTm8

MIT News writes "Can robots learn from machine dreams? MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI's potential for creating robotics training data." https://lnkd.in/esTBFEQM. #mitcsail #generativeai #imagegeneration #artificialintelligence #lucidsim #robotics #trainingdata #mitnews