1. ARS: AI-Driven Recovery Controller for Quadruped Robot Using Single-Network Model
- Author
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Han Sol Kang, Hyun Yong Lee, Ji Man Park, Seong Won Nam, Yeong Woo Son, Bum Su Yi, Jae Young Oh, Jun Ha Song, Soo Yeon Choi, Bo Geun Kim, Hyun Seok Kim, and Hyouk Ryeol Choi
- Subjects
legged robot ,reinforcement learning ,fall recovery ,Technology - Abstract
Legged robots, especially quadruped robots, are widely used in various environments due to their advantage in overcoming rough terrains. However, falling is inevitable. Therefore, the ability to overcome a falling state is an essential ability for legged robots. In this paper, we propose a method to fully recover a quadruped robot from a fall using a single-neural network model. The neural network model is trained in two steps in simulations using reinforcement learning, and then directly applied to AiDIN-VIII, a quadruped robot with 12 degrees of freedom. Experimental results using the proposed method show that the robot can successfully recover from a fall within 5 s in various postures, even when the robot is completely turned over. In addition, we can see that the robot successfully recovers from a fall caused by a disturbance.
- Published
- 2024
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