Figure 02 Humanoid Robot Learns Natural Walking Movement Through RL
You’ve seen Suzuki’s MOQBA, a 4-wheeled walking motorbike concept, now check out the Figure 02 humanoid robot showing off its new natural walking movement. How was this achieved? Through reinforcement learning (RL) of course, or more specifically, a method where an AI learns by trial and error in a simulated environment.
What makes this approach different from just having the robot watch real-life humans? Well, RL allowed Figure’s engineers to compress years’ worth of walking data into just hours of training, seamlessly blending physics simulations with real human motion data. The result? A humanoid robot that walks more fluidly, without the stiff, mechanical gait you’d typically expect to see.
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Think of this as a solid step forward rather than a flawless human imitation. Figure 02’s gait is smoother, but as you can see, it’s still a work in progress. The training hinges on an end-to-end neural network, which means the system learns everything from scratch, optimizing each movement to mimic natural walking.
Figure 02 Humanoid Robot Learns Natural Walking Movement Through RL
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