📹 I don't think this is just another tutorial contest. Axis Robotics is turning its own community into educators. A great tutorial doesn't just help one person—it helps hundreds of new users onboard faster, learn the platform more efficiently, and ultimately contributes to a stronger robotics ecosystem. To me, that's why this campaign matters. If you've been exploring @axisrobotics , this is a great opportunity to share your knowledge and get rewarded. 🏆 Prizes: 🥇 Best Tutorial — 200 USDT Role Promotion 🥈🥉 2nd & 3rd Place — 50 USDT each ⭐ Best Engagement in Each Region — Role Promotion 📅 Submission Window: 🗓️ July 8, 2026 (00:00) → July 9, 2026 (00:00) Requirements: ✅ Publish your tutorial on X ✅ Include English subtitles ✅ Tag @axisrobotics ✅ Cover key features like Click & Drag, Double Click, Multi-Embodiment Tasks, and other essential functions ✅ Include the keywords "train-to-earn" and "zero costs to participate" in your local language The best communities aren't just built by developers—they're built by members willing to teach others. #AxisRobotics #PhysicalAI #Robotics #TrainToEarn #AI #Web3 #RobotLearning

🚨 Only 1 week left to apply! Join the 2nd Robotic Collaborative Assembly (RoCo) Challenge @ #IROS2026 🤖 🏭 Industrial Board Assembly 🧱 Brick Assembly ⏰ Proposal deadline: Jul 12, 2026 Take a look at the accompanying demonstrations to see robotic manipulation in action 👀 1️⃣ DexMate bimanual precision assembly in simulation 2️⃣ Sharpa North teleoperation with tactile sensing 3️⃣ Collaborative LEGO assembly in simulation 4️⃣ Real-world bimanual LEGO assembly 🚀 Challenge: rocochallenge.github.io/RoCo… 📝 Register: forms.gle/d2NKNAE7dqSfYZB87 ❓ FAQ: rocochallenge.github.io/RoCo… 💬 Discord: discord.gg/BvxEN5vAh3 #RoCoChallenge #Robotics #PhysicalAI #EmbodiedAI #RobotLearning #RobotAssembly #IndustrialRobotics #BimanualManipulation #Simulation #Research #IROS2026

🚀 Expanding our LATAM data collection network. 🏠 1,000 operators for residential data collection 🏭 2000 commercial facilities for real-world data capture Looking to partner with Physical AI, robotics, & VLA companies #PhysicalAI #EmbodiedAI #Robotics #VLA #RobotLearning

『両腕・両手・36自由度の器用な操作を学ぶオープンソースVLA』 掴むだけでなく、支える・回す・開ける・使う、ロボットの“手仕事AI” dexoravla.github.io/ #dexterous #RobotHand #ｍanipulation #DualArm #OpenSource #RobotLearning #PhysicalAI #EmbodiedAI #VLA #DISCOVERrobotic

AI Sapiens K1’s Sim2Real package connects robot feedback, operator/API input, policy inference, and joint command publishing into a 1000 Hz control loop for real-robot deployment. Built-in modes include Damping, ReadyPose, Velocity, Mimic, and more, with failsafe logic checking RC input, IMU, joint states, policy actions, and body orientation before execution. Train. Validate. Deploy on hardware. More AI Sapiens K1 development info coming soon. Docs: docs.robotis.com/docs/system… #ROBOTIS #AISapiens #Sim2Real #PhysicalAI #HumanoidRobot #Robotics #ROS2 #DYNAMIXEL #RobotLearning

Most manipulation datasets are collected in isolation. Most manipulation tasks are not. The demos you see online prove the robot can do the task. They don't prove it can do the task with a person in the workspace. Those are different problems, and most training data only covers one of them. KAIST just released HABIT, and it's built to close exactly that gap. 10,563 episodes. A co-present human in every single one. Three roles: Collaborator, Coworker, Supervisor. Each role isolates a different failure mode: precondition violations, collisions, gesture-following errors. What I like here is the method. They didn't just put a human in frame and call it interactive data. They defined the roles first, then structured collection to force the specific behaviour each role demands: yielding under Coworker, gesture grounding under Supervisor, spatiotemporal sync under Collaborator. That's the difference between data that looks realistic and data that's actually diagnostic. The results back it up. Fine-tuning π0.5 on HABIT beat a matched robot-only baseline on every comparable task, with the biggest gains on Coworker, where reactive yielding resolves path conflicts. GR00T N1.6 shows the same pattern at lower absolute numbers, which tells you the gain is coming from the dataset, not the architecture. Mid-training on HABIT compounds too: 100 task-specific demonstrations after mid-training beat 200 demonstrations of direct fine-tuning on shelf-cleaning. I've said this before about sample efficiency and I'll say it again about coordination: this isn't a scale problem, it's a data problem. If human presence changes behaviour this cleanly, a human-absent dataset isn't incomplete, it's blind to the failure modes that actually matter once the robot leaves the cell. Robot-only pre-training gets you competence. It does not get you coordination. The next generation of training data needs an independent human moving through the scene unpredictably, not another 10,000 episodes of the same skill performed alone. [Paper and dataset link in comments] Congratulations to the team at KAIST on the release. #Robotics #PhysicalAI #RobotLearning

[7/7] Joint work with @jnsungp and me. Huge thanks to @carlo_sferrazza, @yswhynot, @xiaolonw for their advice! 📄 Paper: arxiv.org/abs/2606.31236 🌐 Code & videos: tactx-project.github.io #Robotics #Manipulation #TactileSensing #RobotLearning

Before a robot can perfect assembly, it needs to learn to play. The team behind SimToolReal @kushalk_ @tylerlum23 @leto__jean @KarenJLiu published another cool paper! Play2Perfect pretrains on diverse, task-agnostic play (grasp, reorient, reach, etc), then finetunes on sparse-reward assembly. Result: 33× sample efficiency vs. training from scratch, and zero-shot sim-to-real down to 0.5mm clearance. Peg insertion, screwing, multi-part assembly: all running at 60Hz, real speed, real hardware. And when a grasp slips, the policy doesn't stop, it recovers and keeps going. The Sharpa Wave responded present again ;) Project: play2perfect.github.io #Robotics #SharpaWave #Sharpa #EmbodiedAI #DexterousManipulation #RobotLearning

Gripper-mounted Ego UMI data collection. This demo explores a new workflow using a head-mounted Insight 9 together with Insight 7 mounted on the grippers. ▪️ Object picking ▪️ Cloth folding Synchronously capturing: ▪️ First-person observations ▪️ End-effector trajectories ▪️ Real-time VIO Not just capturing data—it enables real-time verification during collection. P.S. In the current demo, gripper opening/closing angle is not yet synced. We'll update this in our next test. Stay tuned! #EmbodiedAI #RobotLearning #Robotics #EgoData #UMI #VIO #PhysicalAI #ComputerVision

ロボット学習に必要なのは、単なるデータ量ではありません。 重要なのは、再現性があり、学習に使える高品質な人間動作データを安定して取得できること。 Noitom Robotics の PNLink / PNS が NVIDIA Isaac Teleop に対応し、ROS 2、Isaac Sim、Isaac Lab をまたいだ統一されたテレオペレーション環境で、高精度なモーションキャプチャデータの活用を支援しています。 #RobotLearning #NVIDIA #PhysicalAI #Mocap #Robotics

Human demos, straight into robot learning. Noitom Robotics now officially supports @NVIDIARobotics’ NVIDIA Isaac Teleop framework. With PNS and PNLink, developers can stream high-fidelity human motion into Isaac Sim/Lab for teleop, retargeting, and clean training data. nvidia.github.io/IsaacTeleop… #Robotics #Humanoids #RobotLearning

『人型ロボットが実作業をしながら学ぶ訓練施設』 物流・製造・小売の現場データを集め、Google DeepMindと協業し、Gemini Roboticsモデルを発展 apptronik.com/news-collectio… #HumanoidRobot #PhysicalAI #EmbodiedAI #RobotLearning #RobotPark #Apollo #Apptronik

What Is a Trajectory? A robot doesn't learn from the outcome. It learns from the journey. Every movement. Every adjustment. Every correction. A trajectory is more than a path it's experience captured as data. That's how robots improve over time. #AxisRobotics #RobotLearning

Many people talk about “AI data,” but very few speak seriously about data quality. In robotics, a large amount of noisy data can actually be more dangerous than having little data. A robot that learns from incorrect data doesn’t just give wrong answers like a chatbot. It can move incorrectly, grasp objects wrongly, misunderstand its environment, and cause real physical errors. That’s why the quality layer isn’t just a nice-to-have feature — it is core infrastructure. I’m paying close attention to @PrismaXai for making VLA Foundry and Verify Quality their first public releases. This is a smart and grounded approach: instead of claiming “we have a powerful AI engine,” they start with something very practical — verifying robot data. This is a wise product strategy if executed well. Before you can sell a good robotics model, you need a trustworthy data generation pipeline. And before that, you need reliable, repeatable evaluation standards. The risk lies in whether those standards are rigorous enough. If the consensus only reflects the opinion of a non-expert crowd, quality won’t necessarily improve. I’ll continue following how they train their validators, measure agreement, and handle edge cases. This is exactly where real product separates from beautiful narrative. 
#Robotics #DataQuality #AI #RobotLearning #VLA #EmbodiedAI #PrismaXai

Sim-to-real workflows just leveled up. Noitom Robotics now supports NVIDIA Isaac Teleop, bringing our PNS and PNLink motion capture systems into a unified teleoperation pipeline for precise, low-latency human input. Get started: nvidia.github.io/IsaacTeleop… #EmbodiedAI #HumanoidRobotics #NVIDIAIsaac #Teleoperation #RobotLearning #SimToReal

📢 Special Issue Call for Papers: Toward Physical Intelligence in Robotics • Embodied perception • Robot learning • VLMs, VLA & world models • Humanoid robots 📅 Jun 20, 2027 📘oaepublish.com/specials/ir.1… #PhysicalIntelligence #EmbodiedAI #RobotLearning #Robotics

『少ない試行で高速・高精度な動きを修正していくロボット』 成功/失敗だけでなく「どちらにどれだけズレたか」を学ぶ、賢い残差学習。 kploeger.github.io/residual-… #RobotLearning #ResidualLearning #PhysicalAI #EmbodiedAI #BallJuggling #Juggling #お手玉

『人型ロボットに“長い仕事をやり切る力”を持たせる知能基盤』 荷物を取り、階段・エレベーターを使い、箱を開け、失敗したら再計画する。 flexion.ai/news/flexion-refl… #HumanoidRobot #PhysicalAI #EmbodiedAI #RobotLearning #LongHorizon #autonomous #ReflectV1 #FlexionRobotics

Proud to be part of the team behind CHORD, advancing dexterous robot learning from human demonstrations through contact-wrench guidance. Congratulations to the entire team on this exciting milestone! #Robotics #PhysicalAI #DexterousManipulation #RobotLearning

🌟 EvoHumanoid.com Humanoid robotics will not stand still. It will evolve. EvoHumanoid.com is built for evolutionary humanoid robotics, next-generation AI robots, adaptive machine intelligence, Physical AI, robot learning, autonomy, and future human-machine evolution. Why it stands out: • Strong “Evo” “Humanoid” future-tech positioning • Shorter and more brandable than many technical robotics names • Ideal for humanoid startups, AI labs, robot evolution platforms, and robotics media • Suggests progress, adaptation, and next-generation intelligence • Moonshot potential as humanoids evolve from prototypes into real-world workers and companions Potential use cases: 🔹 Humanoid Robotics Brand 🔹 Evolutionary AI Robot Platform 🔹 Physical AI Research Lab 🔹 Adaptive Robot Intelligence 🔹 Humanoid Autonomy Software 🔹 Robot Learning System 🔹 Future Robotics Media 🔹 Human-Machine Evolution Hub 🔹 Service Robot Startup 🔹 Next-Gen Humanoid Ecosystem EvoHumanoid.com is not just a domain. It is a premium digital asset for the evolution of humanoid robotics, embodied AI, and intelligent machines. #EvoHumanoid #HumanoidRobots #HumanoidRobotics #EvolutionaryAI #PhysicalAI #EmbodiedAI #RobotLearning #AdaptiveAI #ArtificialIntelligence #Robotics #AutonomousRobots #MachineIntelligence #FutureOfRobotics #FutureOfAI #ServiceRobots #HumanMachineInteraction #DeepTech #TechStartup #StartupBranding #PremiumDomain #DomainName #DomainInvestor #DigitalAssets #BrandableDomain #Moonshot #EvoHumanoidCom