An exploration of modeling and control methods for bipedal humanoid robots




Cruz, Melissa Jordan

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Two whole-body motion planning and control methods are presented in this report: trajectory generation and tracking using Centroidal Dynamics and optimization methods and using Reinforcement Learning (RL). Centroidal Dynamics utilizes a simplified model of the robot by assuming that all of the robot’s mass is located at the center of mass of the robot. This assumption greatly reduces the computational cost at the expense of a less accurate robot model. The RL trajectory generation and control is implemented using NVIDIA’s Isaac Gym environment. Isaac Gym massively parallelizes computation by using available GPUs, greatly decreasing computation time, making it a useful tool to develop standing and walking policies using RL on humanoid robots. Both methods produced trajectories that resulted in stable XY-planar movement. The Centroidal Dynamics method produced more promising results, with stable Z movement. More work should be done on the RL method regarding reward tuning.


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