RL + Model-based Control: Using On-demand Optimal Control to Learn
Versatile Legged Locomotion
- URL: http://arxiv.org/abs/2305.17842v3
- Date: Mon, 4 Sep 2023 11:34:54 GMT
- Title: RL + Model-based Control: Using On-demand Optimal Control to Learn
Versatile Legged Locomotion
- Authors: Dongho Kang, Jin Cheng, Miguel Zamora, Fatemeh Zargarbashi, Stelian
Coros
- Abstract summary: This paper presents a control framework that combines model-based optimal control and reinforcement learning.
We validate the robustness and controllability of the framework through a series of experiments.
Our framework effortlessly supports the training of control policies for robots with diverse dimensions.
- Score: 18.0248682206808
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper 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.
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