The bound gait is an important gait in quadruped robot locomotion . It can be used to cross obstaclesand often serves as transition mode between trot and gallop . However, because of the complexity of the models, the bound gait built by the conventional control method is often unnatural and slow to compute. In the present work, we introduce a method to achieve the bound gait based on model-free pre-fit deep reinforcement learning (PF-DRL) . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points . We also used feature engineering to improve the input features of the DRL model and improve performanceon flat ground. Finally, we trained the bound controller in simulation and successfully deployed it on the Jueying Mini robot . It performs better than the conventional method with higher computational efficiency and more stable center-of-mass height in our experiments.
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.
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The bound gait is an important gait in quadruped robot locomotion . It can be used to cross obstaclesand often serves as transition mode between trot and gallop
Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles
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because of the complexity of the models, the bound gait built by the conventional control method is often unnatural and slow to compute. In the present work, we introduce a method to achieve the bound gait based on model-free pre-fit deep reinforcement learning (PF-DRL) . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected
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
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that used
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controller
controllers
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trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding,
pretrained DNN weights are optimized further via deep reinforcement learning (DRL). Also,
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based on contact points . We also
considering contact points to enforce the gait symmetry and periodicity, and
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the
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improve performanceon flat ground. Finally, we trained the bound controller in simulation and successfully deployed it on the Jueying Mini robot . It performs
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
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conventional method with higher computational efficiency and more stable center-of-mass height in our
previous model-based control in terms of robustness and stability in both indoor and outdoor