Imagine training a robot to fold clothes, only for it to struggle because the arm has changed.O̢̧̡̡̢̡̧̡̢̢̧̢̢̧̢̧̧̢̨̧̢̡̡̨̡̨̧̨̡̡̢̨̧̡̲̲͙̪̼̥̼͈̣͈͉̖̖̯͔̹̜̦̦̘̹̲͖̺̘̭͍͇̟͚̯̗̝̟̗̻͎̭̱͇̣̘̼̜̤̥͔̜̟͎̼͎̰̤̭͍̲̪̯̺͈̰̬͇̰̩̗̝͇̖͇̜͎͕͉̦͓͕̬͎͖̟̣͙̮̭̮͕̫̞̜̣̹̯̘͉̲͔̖̮͇͈̰͎̜̺̲̼̙̪̙̪̘̲̱̜̭͚̼̙̠͚͉̼̗̥̯̯͕̹͇͔̼̫̠͍͔̩̠̖̰̠̗͔̬͚͔̲̱̠̠̞̱̞̣̗͚̺̜̟͚̪̠̫͍̬̱͇̦͖̣̣̰̤͎̘͕͈̙̖̰̻̜̱͓̣̦͕̬̘̰̫̪͈͎͇͓̞͍͕̙͈ That is one of the problems @PrismaXai is working to address. Different robot arms have different joints, motors and controllers, training AI on one setup doesnt automatically mean it can handle another. PrismaX connects real robot operation with Physical AI training data, across different hardware. The current validated fleet includes: • Piper by AgileX • TOK2 by Airbot • YAM by I2RT Each listed robot goes through setup validation, with onboarding support included. Already own a robot? Supported makers include Airbot, AgileX, I2RT Robotics and Realman, and you can register eligible hardware for data collection. Here is why this matters 👇 A robot learning to pick, sort, pack or fold should not just learn the quirks of one arm. It needs useful demonstrations across different tasks and hardware. PrismaX also offers data access plans for different needs, from 4 robot arm types on Discover to 8 on Researcher and 12+ on Enterprise. One platform, different robot arms, more ways to collect data for Physical AI. Follow PrismaX for more info Start teleoperating and validating here: app.prismax.ai/

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