Efficient learning of robust quadruped bounding using pretrained neural networks

Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles. The authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body movements. The authors first pretrained the neural network (NN) based on data from a robot operated by conventional model-based controllers, and then further optimised the pretrained NN via deep reinforcement learning (DRL). In particular, the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity, which improved the bounding performance. The NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully. A variety of environments are presented both indoors and outdoors with the authors’ approach. The authors’ approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven terrain

Bio-inspired design of electricallydriven bounding quadrupeds via parametric analysis

a b s t r a c t This paper attempts to set the basis for a systematic approach in designing quadruped robots employing a dynamically stable quadruped running in the sagittal plane with a bounding gait, which is a simple model commonly used to analyze the basic qualitative properties of quadruped gaits that use the legs in pair. The outcome of the proposed methodology is the optimal shape of the bounding quadruped robot, i.e., the relation between its physical parameters, and the optimal size of the bounding quadruped robot, i.e., the physical magnitude of it, according to desired performance criteria. The performance criterion introduced is based on: (a) the actuator effort to sustain an active gait, very close to a passive one, and (b) the maximum payload capability of the robot for a target overall mass. The parametric study examines the behavior of the performance criterion over a range of non-dimensional variables connected to robot physical parameters and gait characteristics. The study takes into consideration data from experimental biology and ground surface properties, while it is subject to the existing technological limitations and economic restraints, i.e., the fact that there is a limited number of motor/gearbox combinations available from a practical point of view. The findings from simulation results indicate that the proposed methodology can assist in the design of new, and modifications of existing quadruped robots

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

Body randomization reduces the sim-to-real gap for compliant quadruped locomotion

Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of physical damage. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. Here, we investigate the impact of randomizing body parameters during learning of CPG controllers in simulation. The controllers are evaluated on our physical quadruped robot. We find that body randomization in simulation increases chances of finding gaits that function well on the real robot