Humanoid robots might be able to run, dance, and occasionally kick people, but to become truly human, they’re going to need to learn how to do all sorts of menial chores at work.
Flexion Robotics, a Swiss startup founded by ex-Nvidia robotics researchers, thinks it has the solution. The company has developed a way to train robots to perform complex tasks that involve simple skills like opening doors, climbing stairs, and carrying boxes. The key is to teach the robots individual skills in simulation, then have a master AI algorithm determine how to use them.
Most demo videos show humanoids that have been trained to do a specific task, like folding shirts or loading shelves. Typically, this is done through teleoperation—a person behind the scenes who controls the robot’s movements. But this approach doesn’t work reliably when the robot is put into unfamiliar settings. Flexion says its system is different—and more efficient—because it trains its robots in simulation and with limited human instruction.
The video below shows the software in action: A modified Unitree humanoid robot operates autonomously after it receives the following command: “A parcel with snacks has been delivered for Flexion. Retrieve it using the stairs and come up using the elevator. Then unpack it and place the items into the empty drawer on the shelf in the snack area.”
Flexion’s approach works by combining different AI systems.
The main AI model figures out how to do its chores by digesting videos of humans doing different things. The software then matches learned skills—which it has picked up in simulation—to the videos and performs those tasks in the real world. In order to reach the mail room in an office, for example, the model may have learned that it needs to open certain doors and use the elevator. The system also controls the robot’s motors, allowing it to walk, move its limbs, and maintain balance.
According to Nikita Rudin, the cofounder and CEO of Flexion and a former robotics research scientist at Nvidia, the software’s “secret ingredient” is its extensive use of reinforcement learning, which trains computers to master tasks through trial and error. Each layer of the software, from the master AI model to the simulation to the motor control, uses this approach.
Tech industry leaders like Elon Musk and Jensen Huang argue that humanoids will have a huge impact on the economy because they may eventually replace a good chunk of human labor. But Flexion’s demonstration reflects the fact that empowering humanoids will require fundamental advances in AI.
“The humanoid itself isn’t the interesting, revolutionary thing, rather it’s the AI models that back them,” says George Chowdhury, an analyst with ABI Research who follows the humanoid market. ABI Research estimates that the market for robot foundation models could be worth $150 billion by 2036.
Rudin says that Flexion is collaborating with a number of robotics companies and notes that it works across different humanoid forms. Given the number of systems on the market, that could make the software more commercially valuable.
Chowdhury says Flexion will need to work closely with hardware manufacturers to succeed and will face fierce competition. But without the ability to program humanoids in the way Flexion demonstrates, he says, “there isn’t really a market here.”
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