Figure AI’s 02 Humanoid Can Now Walk Naturally Like a Human
Figure AI, the California-based robotics company, has introduced a humanoid robot capable of walking with human-like movements. The breakthrough, announced on Tuesday, aims to enhance the robot’s adaptability for industrial and domestic applications by mimicking natural human locomotion. The innovative approach compresses years of simulated training into hours. Introducing learned natural walkingFigure can now walk naturally like a humanYears of data was trained in simulation in just a few hours pic.twitter.com/HvOq9s1krS— Figure (@Figure_robot) March 25, 2025 Using reinforcement learning (RL), the robot learns through trial-and-error simulations to balance, shift weight, and walk naturally. CEO Brett Adcock took to X to announce this, saying, “Say goodbye to the Biden Walk!” This training is transferred directly to real-world hardware using domain randomisation and high-frequency torque feedback, enabling zero-shot deployment without additional fine-tuning. Sim-to-Real TransferBy combining domain randomization in simulation with high-frequency torque feedback on the robot, policies trained in sim transfer zero-shot to real hardware without additional tuning pic.twitter.com/QihAWYmHfl— Figure (@Figure_robot) March 25, 2025 Human-Like Gait and Scalability Figure AI’s training process simulates thousands of virtual humanoids in parallel under varied physical parameters and scenarios. These include changes in terrain, actuator dynamics, and external disturbances like trips or slips. The Figure 02 robot achieves this through an end-to-end neural network trained via reinforcement learning (RL) in a high-fidelity physics simulator. Trained in SimulationOur robot learns to walk naturally similar to a human via a high fidelity physics simulatorWe simulate years of data in only a few hours pic.twitter.com/Io7XKaena9— Figure (@Figure_robot) March 25, 2025 As per the company’s announcement, the robots are exposed to a wide range of scenarios they might encounter, and a single neural network policy learns to operate them all. The robots now exhibit features such as heel strikes, toe-offs, and synchronised arm swings. According to the company, these improvements were achieved by rewarding the robot for mimicking human walking trajectories while optimising for velocity tracking, energy efficiency, and robustness. Figure AI showcased 10 Figure 02 robots operating on the same RL neural network without modifications. This consistency emphasises scalability across its fleet of robots without manual adjustments. “This gives us hope this process can scale to thousands of Figure robots in the near future,” the company stated. Adcock also added that “2025 will be a pivotal year as we start production, ship more robots, and tackle home robotics.” Industry Implications The company mentioned in a post on X, “This year is going to be a big one for Figure. We’re launching into production manufacturing, scaling up robots at our commercial customers, and working on launching robots into the home.” The technology addresses labor shortages and safety concerns while opening possibilities for broader applications. The company plans to scale production, manufacturing, and commercial deployment later this year. This development positions Figure AI as a competitor in the humanoid robotics space alongside Tesla’s Optimus and Agility Robotics’ Digit. Not just these, but also alongside Chinese competitors like UBTECH Robotics and Unitree Robotics. The company recently announced Helix, a Vision-Language-Action (VLA) model that allows humanoid robots to perform complex tasks using natural language. This marked a leap in AI for humanoids before any other company. This model marked progress in robotics, enabling robots to understand and react to instructions in real time, handle unforeseen objects, and collaborate.
The company has achieved this through an end-to-end neural network trained using reinforcement learning.
The company has achieved this through an end-to-end neural network trained using reinforcement learning.
["AI in Robotics", "Figure AI", "Robotics"]
["Go", "programming_languages:R", "AI", "neural network", "programming_languages:Go", "Scala", "Robotics", "AI in Robotics", "Git", "Aim", "programming_languages:Scala", "Figure AI", "R"]
["AI", "neural network", "Aim", "R", "Go", "Scala", "Git", "programming_languages:R", "programming_languages:Scala", "programming_languages:Go"]
An information designer who loves to learn about and try new developments in the field of tech and AI. She likes to spend her spare time reading and exploring absurdism in literature.