Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks
| Autor: | Li, Anqiao, Wang, Zhicheng, Wu, Jun, Zhu, Qiuguo |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Popis: | Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles. However, due to a large number of robot and environmental constraints, conventional planning and control has limited ability to adapt bounding gaits on various terrains in real-time. We proposed an efficient approach to learn robust bounding gaits by first pretraining the deep neural network (DNN) using data from a robot that used conventional model-based controllers. Next, the pretrained DNN weights are optimized further via deep reinforcement learning (DRL). Also, we designed a reward function considering contact points to enforce the gait symmetry and periodicity, and used feature engineering to improve input features of the DRL model and the bounding performance. The DNN-based feedback controller was learned in simulation first and deployed directly on the real Jueying-Mini robot successfully, which was computationally more efficient and performed much better than the previous model-based control in terms of robustness and stability in both indoor and outdoor experiments. 7pages |
| Databáze: | OpenAIRE |
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