Why Does PrismaX Use Multiple Camera Views? 📷🦾 Imagine trying to pick up an object while only being allowed to look at it from one fixed angle. Sometimes you would see everything clearly. Other times, the robot itself might block the object. That is one reason multiple camera views are so useful in robotics. 1. One Camera Cannot See Everything A single camera gives one perspective of the workspace. But robot tasks happen in 3D space. An object can be: • Behind the gripper • Partially hidden • Too close to judge properly • Blocked by the robot arm • Difficult to see from one direction Another camera angle can reveal information the first camera misses. 2. The Overhead View Shows the Workspace PrismaX's published robotics data standards emphasize having a view that can clearly show the relevant workspace. An overhead perspective can help show: • Where objects are located • Where the robot is • The overall task area • How objects move during the task It gives useful context for the full demonstration. 3. Wrist Cameras Show the Action Up Close Cameras closer to the robot's gripper can give a very different perspective. They can help capture: • The approach to an object • Gripper alignment • Contact with the object • Small movements during manipulation The overhead camera shows the bigger picture. A wrist-level view can show the details. 4. Different Views Help With Understanding Imagine the robot picks up a cup. From one angle, you might clearly see the robot approaching it. From another angle, you might better see whether the gripper actually made proper contact. Multiple views provide more information about the same action. 5. It Also Helps With Robotics Data Camera views are not only useful for the teleoperator. They can also become part of the recorded demonstration. That gives models and researchers more visual information about: • The environment • The robot • The object • The action • The result The Simple Idea One camera sees one perspective. Multiple cameras can capture more of the task. Overhead View → Workspace Context Wrist View → Close-Up Interaction Together, they provide a much clearer picture of what actually happened during a robot demonstration. And when you are trying to understand physical actions, perspective matters. 🦾📷 @PrismaXai | @vivianrobotics | @MaxC16134

The internet never had a judge. AI agents are about to move $9 trillion. Nobody built the part that handles disagreement. GenLayer did. And now you can become part of it: portal.genlayer.foundation/g…

What Happens to a Teleoperation Session After It Ends? 🦾➡️📊 You complete the task. The robot stops moving. The teleoperation session ends. But what happens to everything that just happened? For robotics data, the end of the session can actually be the beginning of another process. 1. The Demonstration Has Been Recorded During a teleoperation task, the system can capture information about what happened. Depending on the collection setup, that can include things such as: • Camera footage • Robot movement • Joint states • Gripper actions • Task progress • The final result Together, this information creates a record of the robot performing the task. 2. The Episode Can Be Reviewed Not every completed session automatically becomes useful training data. The demonstration may need to be checked. Reviewers can look at things such as: • Was the correct task performed? • Was the workspace clearly visible? • Was the task completed? • Were the movements usable? • Were there major mistakes? • Does the episode meet the required standard? This is where quality review becomes important. 3. Useful Demonstrations Can Be Organized Once a demonstration passes the required checks, it can be organized with other related episodes. For example, episodes may be grouped by: • Task • Environment • Robot • Object • Collection type This turns individual teleoperation sessions into a more useful dataset. 4. Robotics Teams Can Use the Data A Physical AI team may need demonstrations of a particular task. Instead of teaching a model from nothing, they can use real-world examples showing how that task was performed. The data can then become part of research, evaluation, or model training. 5. The Full Flow A teleoperation session can move through a much bigger pipeline: Teleoperator ↓ Real Robot Task ↓ Recorded Episode ↓ Quality Review ↓ Organized Dataset ↓ Model Training That is why one teleoperation session can matter beyond the few minutes spent controlling the robot. The Bigger Picture To the operator, it may feel like one completed task. But from the data side, it can become one example of how a robot should interact with the physical world. Repeat that across different tasks, objects, positions, and environments, and individual sessions begin to form something much larger: A robotics dataset. That is how teleoperation can connect human control today with Physical AI training tomorrow. 🦾 @PrismaXai | @vivianrobotics | @MaxC16134

TMF Pass applications are open! 200 will be approved. A few tips: 🪑 Quality over quantity. A short, genuine post beats a confusing X article every time. 🪑 AI is allowed, but try without it. 🪑 Don't rush. You have until Saturday morning to submit. themutual.fun/allowlist

742 Greenfield Citizens on @RobinhoodApp Free Mint evergreen742.xyz

Startale App just upgraded its wallet 👀 your assets have been successfully migrated to a new wallet address, while your legacy address remains accessible. the upgrade also unlocks Ethereum features alongside Soneium, giving the account broader EVM functionality. nothing to panic about if you see this migration screen, your assets have already been moved successfully. > new address → active wallet > legacy address → still viewable > soneium + Ethereum → unlocked @StartaleApp @StartaleGroup @0xRamz