Revolutionary Quadrupedal Robot Control Technology Unveiled by KAIST Research Team

Researchers at the KAIST Department of Mechanical Engineering have made a breakthrough in quadrupedal robot control technology, allowing robots to walk robustly and with agility on terrain such as sandy beaches. Led by Professor Hwangbo Jemin, the team developed a technology to model the force received by a walking robot on granular materials like sand and simulated it via a quadrupedal robot.

They also created an artificial neural network structure capable of making real-time decisions to adapt to various types of ground surfaces without prior information while walking at the same time and applied it to reinforcement learning. The trained neural network controller is expected to expand the scope of quadrupedal walking robots by proving its robustness in changing terrain, including the ability to move at high-speed even on a sandy beach and walk and turn on soft grounds like an air mattress without losing balance.

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The research team defined a contact model that predicted the force generated upon contact from the motion dynamics of a walking body based on a ground reaction force model that considered the additional mass effect of granular media defined in previous studies. By calculating the force generated from one or several contacts at each time step, the deforming terrain was efficiently simulated.

The research team also introduced an artificial neural network structure that implicitly predicts ground characteristics by using a recurrent neural network that analyzes time-series data from the robot’s sensors. The learned controller was mounted on the robot RaiBo, which was built by the research team to show high-speed walking of up to 3.03 meters/second on a sandy beach where the robot’s feet were completely submerged in the sand. Even when applied to harder grounds, such as grassy fields and a running track, RaiBo was able to run stably by adapting to the characteristics of the ground without any additional programming or revision to the controlling algorithm.

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In addition, it rotated with stability at 1.54 rad/s (approximately 90° per second) on an air mattress and demonstrated this quick adaptability even in the situation in which the terrain suddenly turned soft.

The research team demonstrated the importance of providing a suitable contact experience during the learning process by comparison with a controller that assumed the ground to be rigid, and proved that the proposed recurrent neural network modifies the controller’s walking method according to the ground properties. The simulation and learning methodology developed by the research team is expected to contribute to robots performing practical tasks as it expands the range of terrains that various walking robots can operate on.

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