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A research team led by Professor Hwangbo Jemin of the KAIST Department of Mechanical Engineering has developed a quadrupedal robot control technology that can walk robustly with agility even in deformable terrain such as ...
Professor Hwangbo's research team developed a technology to model the force received by a walking robot on the ground made of granular materials such as sand and simulated it via a quadrupedal robot. Also, the team worked on an artificial neural network structure that was 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.
This research, with Ph.D. Student Soo-Young Choi of KAIST Department of Mechanical Engineering as the first author, was published in January in Science Robotics, titled "Learning quadrupedal locomotion on deformable terrain."
Reinforcement learning is an AI learning method used to create a machine that collects data on the results of various actions in an arbitrary situation and utilizes that set of data to perform a task. Because the amount of data required for reinforcement learning is so vast, a method of collecting data through simulations that approximates physical phenomena in the real environment is widely used.
Adaptability of the proposed controller to various ground environments. The controller learned from a wide range of randomized granular media simulations showed adaptability to various natural and artificial terrains, and demonstrated high-speed walking ability and energy efficiency. Credit: The Korea Advanced Institute of Science and Technology (KAIST)
Contact model definition. (A) The terrain model predicts a vertical component of aground reaction force based on the intruder’s penetration depth and velocity. The calculationinvolves the developing granular cone beneath the intruder, as Aguilar et al. (27) proposed.(B) The surface contact between the intruder and the adjacent substrates is approximated as apoint contact at the deepest point. The bulk tangential force from the substrates is assumed tobe Coulomb friction. (C) The horizontal stroke resistive force model is introduced to simulatethe reaction from the substrates when the intruder moves horizontally in the substrates. Theforce is computed based on the travel distance dHSR and the current penetration depth zt. Credit: The Korea Advanced Institute of Science and Technology (KAIST)