Jun 6, 2022 · We demonstrate that users can interactively control soft robot locomotion and switch among multiple goals with specified velocity, height, and ...
Jan 28, 2022 · The paper proposes to learn neural network controllers in a differentiable simulator for locomotion tasks. A set of technical improvements are ...
This work presents a practical learning framework that outputs unified NN controllers capable of tasks with significantly improved complexity and diversity ...
Complex locomotion skill learning via differentiable physics · Installation · Train · View train statstic · Interactive control · Evaluate · About · Releases · Packages ...
Jun 7, 2022 · Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers. However, existing work typically ...
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Co-authors ; Complex locomotion skill learning via differentiable physics. Y Fang, J Liu, M Zhang, J Zhang, Y Ma, M Li, Y Hu, C Jiang, T Liu. arXiv preprint ...
My research focuses on differentiable physical simulation and applying machine learning ... Complex locomotion skill learning via differentiable physics. Yu Fang ...
Apr 3, 2024 · Abstract—The emergence of differentiable simulators en- abling analytic gradient computation has motivated a new wave of learning algorithms ...
Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers. Diversity. 2.