Ben

Strike Robot x Venice: Privacy-Focused AI Infrastructure for Robots @StrikeRobot_ai faced one of the most critical problems when building an AI infrastructure for autonomous robots: which inference engine could run fast, secure, and uncensored? A solution was needed! As everyone knows, the answer was found with @AskVenice. Venice is a privacy-focused and uncensored AI inference platform. Strike Robot chose this platform as the primary inference backend for SR Agentic and SR Platform. This partnership isn't just a technical integration, my friends; Venice is also providing credit sponsorship and co-developing with Strike Robot – a huge step forward! What's changing technically? Inside SR Agentic, Venice now runs as a VLM (Vision-Language Model) reasoning engine. Visual understanding in complex environments, multi-step reasoning, and natural language reporting all pass through Venice. Thanks to the OpenAI-compatible API, integration is clean and fast, and the edge loop remains in place. The two layers don't do each other's work; each performs its own task. This partnership is particularly critical for $SR. Venice's privacy infrastructure + Strike Robot's autonomous systems coming together is not just a technical integration, but redefines how robotic AI is trained.

I remember that when I competed in the national science research competition, I also researched this quadruped robot topic. Back then, I focused on the range of motion and trajectory of the robot's legs through forward and inverse kinematics. Today, the development of artificial intelligence has enabled robots to move across various terrains and easily adapt to complex environments. The development of @StrikeRobot_ai has also progressed from basic foundations to more complex technologies such as VLM and World Modeling.

Coming soon ...

That's point!! world model

Team keeps updating GitHub — they’re cooking something with the $SR platform. Once again, I’ll say this: Did you know that $VVV, now at a $1.34B valuation and one of the best-performing AI projects on Base, chose @StrikeRobot_ai as its first robotics partner? (i can't believe why $SR so low) $POD is going parabolic. $cyb3rwr3n hit ATH. Next: $SR to $20M market cap. github.com/orgs/StrikeRob…

The evolution of StrikeRobot: VLM + Action model -> VLA -> ARLMA? Robots can learn on their own through other models. Humans don't need to define reward functions for the robots; the robots will find their own reward functions.

We are pioneers in using the latest technologies in Web3 to move towards a comprehensive A2A service in the future.

パリのロボティクススタートアップGenesis AIが、世界初の「人間レベル物理操作能力」を実現するロボットAIモデル「GENE-26.5」を公開 卵を割る、ピアノなど人が手袋して動かしてるように見えるレベル

Why Robots work in Simulation but fail in Reality One of the most frustrating moments in robotics: Everything works perfectly in simulation. Then you deploy it on a real robot and suddenly: The grasp misses The arm shakes The robot drifts Contact becomes unstable The motion looks correct, but the task still fails How do you solve the sim to real problem? At first, it sounds simple. Just move the code from simulation onto hardware. But the gap between simulation and reality is much larger than most people think. Simulation environments are extremely clean. The table is flat. Object geometry is accurate. Friction is predefined. Sensors are stable. Robot joints behave exactly as expected. But the real world is messy. Lighting changes. Depth sensors drift. Objects reflect light differently. Motors have delay. Joints have backlash. Contact forces behave unpredictably. And robotics is a chain reaction. A small perception error becomes a planning error. The planning error becomes a control error. The control error becomes an execution error. Eventually, the robot misses the grasp by a few centimeters and the entire task fails. And The hardest part is usually contact. Humans think tasks like: grasping a cup, opening a door, inserting an object, pushing a box are trivial. For robots, these are extremely difficult because contact is not clean physics. A tiny shift in friction, force, or surface geometry can completely change the result. In simulation, objects are usually “well behaved.” In reality: objects slip contact points shift surfaces deform collisions happen unexpectedly This is why many robotic tasks fail not because the policy is fundamentally wrong, but because reality itself introduces uncertainty. Sensors are also less reliable than people think. The robot’s perception already contains error: camera noise unstable depth estimation occlusion pose estimation drift changing lighting conditions Sometimes the model itself is fine, but the input is already slightly wrong. By the time the error propagates to the end effector, the grasp fails. The robot hardware itself is also imperfect. Motors have latency. Controllers have frequency limits. Actuators have error. Different loads change behavior. In simulation, the robot follows commands perfectly. In reality, it may move slightly slower, slightly off target, or slightly unstable. Those tiny differences are fatal in robotics because robots physically interact with the world. Sim2Real being difficult does not mean simulation is useless. Simulation is still incredibly valuable: they are cheap, safe, scalable and reproducible. A better way to think about simulation is: Simulation is the training ground, not the final battlefield. Modern Sim2Real methods usually combine multiple approaches: making simulation more realistic, adding domain randomization, randomizing lighting, friction, object positions, and sensor noise, fine-tuning with real-world data. The goal is not to make the robot adapt to one perfect virtual world. The goal is to make the robot robust enough to survive an imperfect real one. The most important lesson in robotics is: Success in simulation is only the first step. The real test begins when the robot touches the real world. Video Credit: Kevin Zakka

1,000 MuJoCo scenes. 46k+ STL meshes. Focus on Unitree G1. Now open-source on @huggingface. Sim-to-real fails when training environments lack diversity. Hand-authoring scenes doesn't scale. So we contributed 1,000 of them — varied geometry, varied object placement, all physics-validated, all MJCF-ready in one line of code. SR Platform is the agentic system behind it: natural-language prompt → structured scene plan → cached/generated CadQuery assets → validated layout → executable MuJoCo scene.

Venice is powering @StrikeRobot_ai's private robotics stack Our step into robotics is built on the same principle behind everything we do: Your AI shouldn't spy on you.

IT'S OFFICIAL! We're announcing our partnership with @AskVenice — a privacy-first, uncensored AI inference platform on @base — as a primary inference API backend for StrikeRobot’s product offerings. The partnership involves Venice's credit sponsorship and co-development with StrikeRobot to become the VLM reasoning and inference layer for robots, starting with SR Agentic and SR Platform. Robotic anonymous training is here!

The @StrikeRobot_ai team is building a plug and play framework for @UnitreeRobotics

Strike Robot is now Top 4 on Datanet Volume Traded on @reppo 🔥 After ~1 month of going public: → ~14M volume traded → 10.1% network share → 13.59M total votes SR Platform – Strike Robot is among the leading datanets, building alongside top contributors in the Reppo ecosystem. Explore more: reppostats.com/analytics

StrikeRobot is one of the projects I’ve been following and involved in quite early on and up to now, it’s still building along a very clear direction: AI + Robotics + Web3 @StrikeRobot_ai At its core, the project focuses on: Autonomous AI agents Direct integration with robotics systems Infrastructure that brings AI from simulation into the real world So it’s not just AI “on screen” anymore it’s AI that can act in the physical world. What keeps me with StrikeRobot isn’t short-term hype, but the direction they’re taking. While most AI projects today are still focused on: models automation or data layers StrikeRobot is tackling a much harder problem: → embodied AI Personally, I see this as the next step for AI. When AI starts to: control robots participate in production / logistics and create real-world value then the narrative shifts beyond just “AI tools” I’ve been following this project for a while, and a few things stand out to me: A relatively consistent long-term vision Strong alignment with major narratives: AI agents + machine economy Building within the Virtuals ecosystem, leveraging attention and the creator layer From a long-term perspective, my view remains the same: 👉 AI won’t stay purely digital 👉 And projects like StrikeRobot are already one step ahead in that direction

@StrikeRobot_ai Agentics and thinking 😍😍

From seeing → to understanding → to following → now to finding. In the last update, our robot learned how to recognize and follow people SR Agentic now evolves from human-following → to goal-driven object search. Give it a task like: → “find the white bottle on the table ” It will: • break down the instruction via LLM task planning • scan the environment in real-time • localize the object in 3D space • navigate and approach autonomously Step by step, capability by capability — from perception → to tracking → to task execution in the physical world. This is how real-world agentic robotics is built

The much awaited and teased launch of $SR by @StrikeRobot_ai is now live on @virtuals_io Sitting at $3.6M FDV with alot of factors that can push the price forward This is the 2nd robotics launch under the new robotics mechanism Fully positioned in virtuals robotics I am ready to get hurt again 🫡

Virtuals is for plug-and-play robotics. Any Unitree G1 can run anomaly detection, safety monitoring, continuous ops right out of the box.