RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion скачать в хорошем качестве

RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion

Supplementary video for the IEEE RA-L paper "RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion" by Dongho Kang, Jin Cheng, Miguel Zamora, Fatemeh Zargarbashi, and Stelian Coros. IEEE Robotics and Automation Letters (RA-L) Abstract: This letter presents a control framework that combines model-based optimal control and reinforcement learning (RL) to achieve versatile and robust legged locomotion. Our approach enhances the RL training process by incorporating on-demand reference motions generated through finite-horizon optimal control, covering a broad range of velocities and gaits. These reference motions serve as targets for the RL policy to imitate, leading to the development of robust control policies that can be learned with reliability. Furthermore, by utilizing realistic simulation data that captures whole-body dynamics, RL effectively overcomes the inherent limitations in reference motions imposed by modeling simplifications. We validate the robustness and controllability of the RL training process within our framework through a series of experiments. In these experiments, our method showcases its capability to generalize reference motions and effectively handle more complex locomotion tasks that may pose challenges for the simplified model, thanks to RL's flexibility. Additionally, our framework effortlessly supports the training of control policies for robots with diverse dimensions, eliminating the necessity for robot-specific adjustments in the reward function and hyperparameters. Acknowledgment: This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme. We express our gratitude to Zijun Hui for his assistance with the robot experiments. Project website: https://donghok.me/rl-plus-model-base... Preprint: https://arxiv.org/abs/2305.17842 Computational Robotics Lab: https://crl.ethz.ch/   / computationalr2   Dongho Kang: https://donghok.me/   / eastskykang   Jin Cheng: https://jin-cheng.me/   / catachiii   Stelian Coros: http://crl.ethz.ch/coros.html