.@Figure_robot’s 100-hour sorting marathon just showed a human worker narrowly beating an autonomous robot, even as his arm nearly gave out. Figure is demonstrating the massive potential of intelligent robots in physical production, while Axis is building the underlying infrastructure to support and scale this robotic intelligence. Robots like these may not beat humans in every direct contest yet. But on Axis, your data can help train them, advance them, and bring them closer to that future. In that sense, the student may one day surpass the master.

Yesterday our founder @chris_anm01 joined the community for an AMA in our Discord channel. We’ve shared a lot on X about the engineering behind our data engine, but this session went much deeper. Chris broke down our actual competitive moat, our commercial roadmap, and the long-term vision for Axis—critical details we haven't fully unpacked here yet. Here are the key takeaways you need to know. 🧵

Axis Robotics Philippines 🇵🇭 – First Ambassador Line-up A strong community is never built by one person alone, but by individuals who believe in the future of Physical AI and are committed to creating long-term impact together. Today, @axisrobotics proudly introduces its first 3 Official Ambassadors for the Philippines community: @MonetaGratia15 — The Live Streamer @Belzky2 — The Referral Queen @Chunkyweb3 — The Content Creator These ambassadors represent the beginning of Axis Robotics’ journey toward strengthening and expanding the Physical AI movement within the Philippines 🇵🇭 More than simply sharing information, they will play an important role in educating, connecting, and empowering the community while helping grow the Physical AI ecosystem locally. And this is only the beginning. Keep contributing. Keep building. Keep growing with the community because the next Axis Robotics Ambassador could be you. #AxisRoboticsPH#

Massive thanks to @basepilipinas and @0xmoonlight_ for for hosting such a dynamic, insightful, and high-energy experience at the 14th BYCIT 🇵🇭 Over 500+ student participants experienced @axisrobotics firsthand - directly training Physical AI through our browser-based robotics platform on their laptops. From teleoperating robotic tasks to contributing real-world data trajectories for robot learning, students got to experience how everyday users can actively participate in building the future of Robotics General Intelligence. This is exactly what Axis is about: Making robotics data generation accessible, scalable, and community-driven. Proud to be the only robotics project at the event alongside the incredible @base PH ecosystem 📷 The future of Physical AI will not be built by a few labs alone - it will be contributed by everyone. Comment which university in Phillipines you want Axis to be there 📷👇

Raffle Announcement 🎉 Participants who complete raids, create at least 1 content post per day, and showcase at least 1 robot simulation will be eligible. 🎁 5 winners will each receive ₱100 GCash 📅 The raffle will run from May 14 to May 23, 2026.

Axis Weekly Last week, we made progress across the full robotics data loop, including task generation, simulation infrastructure, model training, and failure recovery. Key updates: - Task generation: We improved TaskGen with better automatic checker generation, stronger multi-embodiment support, and more efficient domain randomization to scale task diversity with less manual design effort. - Simulation infra: We continued improving MuJoCo verify/replay and scene-variant workflows, including fixes across data collection, multi-asset scenes, repeated loading/downloads, initial states, teleoperation, IK, and gripper control. - Model training: We confirmed that the new randomized tasks are learnable with sufficient data. In our current experiment, 500 demos successfully produced an executable policy, while 100 demos were not enough. - Failure recovery: We began building a recover-from-failure pipeline to collect and categorize gripper failure and near-failure states during grasping, which will later support more robust recovery policy learning. A closer look at this week’s progress🧵

🎉 Congratulations to All Winners of the Axis Robotics Duck Race Event! 🦆🚀 A huge thank you to everyone who joined and supported the event. The community energy and participation made this competition a success! 🔥 🏆 Special Congratulations to our X Role Winner: ✨ Korogane05 🎁 30 IP Winners: • MaxineCutie • Matynee18 • harty115 • jakntyr • CCLThund3r • jui891 • krizzymoneta • chely2086 • Delegation06 • BelzkyBounty Thank you for continuing to support the Axis Robotics ecosystem and community activities. More exciting events, rewards, and opportunities are coming soon stay active and keep building with us! 🚀 #AxisRoboticsPH#

🚀 AXIS ROBOTICS DUCK RACE EVENT 🦆 The race is officially on! Get ready for 10 exciting rounds of Duck Race and a chance to win IP rewards plus exclusive community roles while supporting the growth of the Axis Robotics ecosystem. 🔥 🏆 Rewards: • 1 Lucky Winner of an X Role • 1 Winner per Race • 10 Total Winners • 30 IP Points per Winner 📅 Date: May 12 ⏰ Time: 10:00 PM 📌 How to Join: 1. Follow: @axisroboticsPh @axisrobotics 2. Join our Telegram Community • Type “/Link” to get the Telegram invite link. 3. Post 1 content about Axis Robotics (1 Content = 1 Duck Entry) 4. Include: #AxisRoboticsPH# 5. Submit your content link under 📍Social Drop 💡 Content Ideas: • Educational posts • AI & Robotics insights • Memes • Threads • Short videos • Quote tweets Help spread awareness about Physical AI and the future being built by Axis Robotics. 🚀 #AxisRoboticsPH#

We are now open-sourcing the AxisDataCleaning pipeline. Github repo: Browser teleoperation is one of the most scalable paths for robot data generation. Raw human input, however, is not yet model-ready: ▪️ Idle pauses ▪️ Micro-jitters ▪️ Low & Variable frame rates Raw web data alone is not enough for reliable policy training. Here is how our backend turns noisy human demonstrations into usable trajectories for downstream policy training. 🧵👇