Axis Robotics (@axisrobotics)

The bonus mechanism is out. TL;DR Everyone gets a 25% unlock at TGE. On top of that, participants will receive additional bonus tokens fully unlocked at TGE, based on their original committed amount before pro-rata dilution. How many bonus tokens do I get? Check out the chart below for the exact formula and allocation examples. Here, x is your original committed amount before pro-rata dilution, and 2,472 represents the total number of Axis believers who decided to ape in even with strict unlock terms.

Grateful to @cicada_mm for featuring us in their research blog. Dive into the full piece for our thinking on scaling data volume without sacrificing quality, how we prioritize robot embodiments, and what foundation-model scale actually means.

From Digital Twins to Data Engine: Cutting the Real-Data Burden with Sim-Powered Robot Learning Teaching a robot a new task could take hundreds of teleoperated demonstrations. For foundation models, adapting to an entirely new robot can cost orders of magnitude more — dedicated hardware, trained operators, months of engineering. On @boosterobotics' dual-arm robot, we studied this at two levels: ✱ Specialist: Can task-aligned simulation mixed with a small set of real demonstrations reduce the real-data burden? ✅ Yes. With only 10 real demos, the policy made no contact at all in physical rollouts (0/20). Adding 50 simulated trajectories brought contact to 17/20. ✱ Foundation: Can data accumulated across tasks build a reusable starting point (a Booster-specific model prior)? ✅ Yes. After full-parameter continued pretraining, a model adapted with just 30 demonstrations per task beat the original given twice as many: 14/16 vs 10/16 in simulated evaluation. Before any task-specific adaptation, in zero-shot simulation, it was already roughly 3× closer to the target (17.27 cm → 5.78 cm). This work runs on Axis Suite, our Physical AI solution across different robot embodiments. Distributed contributors generate task-aligned sim data on Axis Hub at scale, reducing real-data needs for specialist adaptation while powering cross-embodiment generalist training. Read the full blog:

Axis Robotics x @BinanceWallet is live. 1,500,000 Axis Points. 30 days. Reserved exclusively for Binance Keyless Wallet users. Teach robots. Sign your data on Base. Get paid in Points from a pool that's entirely separate from the main product. How it works:

Axis Hub Update: The Pause Button is LIVE. ✱ Click once — the button lights up and the simulation pauses between your actions. Click again to deactivate. ✱ Pre-training: when active, the simulation pauses after each move so you can think before the next. When off, the timer runs nonstop once you start. ✱ Post-training: each takeover is capped at 8 steps — and they run out fast. Pause lets you plan each move without burning through your budget. See it in action ⬇️ Why we cap interventions at 8 steps → see the quoted thread.

Axis Hub update: The Signal Boost campaign is now live in your Portfolio. How to participate: 1️⃣ Navigate to Portfolio (or click the Signal Boost banner). 2️⃣ Select "X Connect & Follow" next to your username to link your X account. 3️⃣ Verify your status to activate the Signal Boost badge and receive 1 Axis Point: Already following: The badge lights up automatically upon connection. Not following yet: Click the button again to follow on X, return, and the badge will unlock. 🔗 Access Axis Hub:

Thanks @baseapac for the clip! @base is for everyone, and our vision is to onboard millions of people to train robots in the physical AI industry. 🔵 The future of robotics isn't just open — it's owned and trained by everyone on base.

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. 🧵