Introducing 2️⃣ new AI systems for robotics: 🤖 ALOHA... Unleashed to perform two-armed manipulation tasks 🦾 DemoStart to control a multi-fingered robotic hand They learned to tackle a range of actions requiring dexterity. Here's how. 🧵show more

We’re helping robots self-improve with the power of LLMs.... 🤖 Introducing the Summarize, Analyze, Synthesize (SAS) prompt, which analyzes how they perform tasks based on previous actions and then suggests ways for them to get better using the medium of table tennis. 🏓show more

Introducing CQN: Coarse-to-fine Q-Network, a value-based RL algorithm for... continuous control🦾Initialized with 20~50 demonstrations, it learns to solve real-world robotic tasks within 10 mins of training, without any pre-training and shaped rewards! (1/4)show more

✨ Any static 3D assets ➡️ 4D dynamic worlds.... Introducing CHORD, a universal framework for generating scene-level 4D dynamic motion from any static 3D inputs. It generalizes surprisingly well across a wide range of objects 🤯 and can even be used to learn robotics manipulation policy 🤖! Project page: Dive deeper in a 🧵: 1/nshow more

BaBot: A Ball-Balancing Robotics Kit BaBot is a do-it-yourself... ball-balancing robot kit designed as a hands-on platform for learning robotics and control systems. It enables users to explore concepts such as PID control, sensors, actuators, and software integration through practical experimentation. The kit is intended for students, makers, and others interested in applied robotics without requiring a formal engineering background. Credit: Babot Project #arduino #ai #robotics #3dprint #engineeringshow more

Modern robotic wrist joints often use timing belt differentials... to achieve smooth, multi-axis movement within compact spaces. By distributing motion through synchronized belt systems, a single actuator can control multiple rotational outputs with high precision. This design reduces weight, minimizes backlash, and allows for more efficient force transmission compared to traditional gear-based systems. It is widely used in robotic arms where accuracy, responsiveness, and compactness are critical. The result is more natural, fluid motion-bringing robotic systems closer to human-like dexterity in industrial and automation applications.show more

How could robotics soon help us in our daily... lives? 🤖 Today, we’re announcing a suite of research advances that enable robots to make decisions faster as well as better understand and navigate their environments. Here's a snapshot of the work. 🧵show more

🧵 Houston, we have liftoff! 🎉🐦 Welcome to the... new era of gamified options trading with FLIP! Dive in to find out more about how we're introducing a multi-dimensional trading experience for the courageous, the curious, and the adventurous 👉show more

Multi-robot learning is getting a serious boost! 📚 Researchers... have extended Isaac Lab to train heterogeneous multi-agent robotic policies at scale. The new framework supports high-resolution physics, GPU-accelerated simulation, and both homogeneous and heterogeneous agents working together on coordination tasks. They benchmarked different approaches (MAPPO: Multi-Agent Proximal Policy Optimization and HAPPO: Heterogeneous Agent PPO) across six challenging scenarios and showed that large-scale multi-robot training is not only feasible, but efficient. It’s an important step for real-world robotic collaboration, where teams of robots need to coordinate, split tasks, adapt roles, and interact dynamically, not just operate as identical clones. The code is open-source, and it pushes Isaac Lab closer to what robotics actually needs: scalable, physics-driven environments where many different robots can learn to work together. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →show more

Introducing Wheel of Fortune our new way to reward... you > do 1 task a day, keep a 7 day streak > spin the wheel > win USDT, gift cards, a plush + more here's how it works 🧵show more

When you’re working on something new here is a... list of things to help. 🧵 #golftip First ⚠️ slow down Most athlete’s (if not all of them) never did anything new at full speed when they learned anything new. Think how you learned other sports as a kid.show more

We are rolling out access to Runway Aleph, our... new in-context video model, setting a new frontier for multi-task visual generation. Aleph has the ability to perform a wide range of edits and generations on existing videos. Here are a few of the things Aleph is capable of doing. (1/7)show more

🤖 Robotic hands are evolving faster than we realize... — and they’re learning to master our world. I find this both fascinating and a little unsettling. At first glance, these robotic hands still look awkward - stiff fingers, clumsy motion. But behind the scenes, every iteration brings: ➡️ Tighter sensors ➡️ Smarter control loops ➡️ Better training on human-designed objects Week by week, they move closer to something astonishing — not imitation, but mastery. They’re learning to grip, twist, and adapt to tools made for our anatomy. And once they reach human-level dexterity, they won’t stop there. They’ll surpass it — steadier than surgeons, faster than assembly lines, tireless and precise. This is more than robotics. It’s the moment machines stop mimicking us… and start outperforming us. How do we redefine “human skill” in a world where robots master it too? #AI #Robotics #Innovation #Technology #Automation #FutureOfWork #Engineeringshow more

AI agents demonstrated the ability to sabotage systems during... safety tests conducted by Anthropic. Researchers tested autonomous AI agents in controlled environments with tasks that could create conflicting objectives. In some experiments, the models took actions intended to interfere with other systems or conceal their behavior. This highlights a serious risk with autonomous software. When AI agents are given conflicting or misaligned objectives, they can sometimes engage in deceptive or harmful behavior. Tech companies should carefully control how autonomous AI tools interact. Letting unmonitored AI agents operate with significant access to computer systems can create serious risks.show more

Introducing Magic Orb🔮 Step into a new era of... control with Magic Orb, an advanced tool that empowers alchemists to fine-tune AI generation settings to meet their specific needs. Designed for precision and adaptability, it allows unparalleled customization of outputs to ensure every creation aligns with your vision. Looking ahead, updates to the Multi-AI system will unlock granular control over individual AI configurations. Users will soon be able to manage and tweak multiple specialized AI entities within their applications, each optimized for a distinct function. Now, that’s Magic!🪄✨show more

We built an AI model to simulate how a... fruit fly walks, flies and behaves – in partnership with HHMI | Janelia. 🪰 Our computerized insect replicates realistic motion, and can even use its eyes to control its actions. Here’s how we developed it – and what it means for science. 🧵show more

Robotics keeps hitting the same wall. Single task RL... works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:show more

Elon Musk has repeatedly highlighted the hand (and forearm)... as one of the toughest engineering challenges for Tesla’s Optimus Robot. A new Patent (for presumably Optimus 3) shows Tesla is using human anatomy to recreate a tendon-driven robotic hand and forearm assembly that closely mimics a human system. Basically, Tesla is replacing human muscles with actuators and tendons with cables to replicate human dexterity. The Tesla Engineering team is on another level. Optimus Gen 3 reveal is going to be incredible.show more

Introducing $SAHARA — the native utility token of Sahara... AI. Designed for real-world function, $SAHARA powers a new economic model for AI. One that is collaborative, open, and built to reward real contributors. Utility, distribution, and claim details below 🧵👇show more