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The real question is how effectively AgiBot’s algorithms can teach its robots new tricks. Using reinforcement learning to teach a robot tasks that require improvisation generally requires a lot of training data, and studies show it cannot be perfected entirely inside a simulation.
AgiBot speeds up the learning process by having a human worker guide the robot through a task, which provides a foundation for it to then learn by itself. Before cofounding AgiBot, chief scientist Jianlan Luo did cutting-edge research at UC Berkeley, including a project that involved robots acquiring skills through reinforcement learning with a human in the loop. That system was shown doing tasks including placing components on a motherboard.
Feng says that AgiBot’s learning software, called Real-World Reinforcement Learning, only needs about ten minutes to train a robot to do a new task. Rapid learning is important because production lines often change from one week to the next, or even during the same production run, and robots that can master a new step quickly can adapt alongside human workers.
Training robots this way requires a lot of human effort. AgiBot has a robotic learning center where it pays people to teleoperate robots to help AI models learn new skills. Demand for this kind of robot training data is growing, with some US companies paying workers in places like India to do manual work that serves as training data.
Jeff Schneider, a roboticist at Carnegie Mellon University who works on reinforcement learning, says that AgiBot is using cutting-edge techniques, and should be able to automate tasks with high reliability. Schneider adds that other robotics companies are likely dabbling with using reinforcement learning for manufacturing tasks.
AgiBot is something of a rising star within China, where interest in combining AI and robotics is soaring. The company is developing AI models for various kinds of robots, including humanoids that walk around and robot arms that stay rooted in one place.
