Galileo: A Pseudospectral Collocation Framework for Legged Robots
<|reference_start|>Galileo: A Pseudospectral Collocation Framework for Legged Robots: Dynamic maneuvers for legged robots present a difficult challenge due to the complex dynamics and contact constraints. This paper introduces a versatile trajectory optimization framework for continuous-time multi-phase problems. We introduce a new transcription scheme that enables pseudospectral collocation to optimize directly on Lie Groups, such as SE(3) and quaternions without special normalization constraints. The key insight is the change of variables - we choose to optimize over the history of the tangent vectors rather than the states themselves. Our approach uses a modified Legendre-Gauss-Radau (LGR) method to produce dynamic motions for various legged robots. We implement our approach as a Model Predictive Controller (MPC) and track the MPC output using a Quadratic Program (QP) based whole-body controller. Results on the Go1 Unitree and WPI HURON humanoid confirm the feasibility of the planned trajectories.<|reference_end|>
@article{chandler2024galileo:,
    title={Galileo: A Pseudospectral Collocation Framework for Legged Robots},
    author={Ethan Chandler, Akshay Jaitly, Mahdi Agheli},
    journal={arXiv preprint arXiv:2409.12465},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12465},
    primaryClass={cs.RO}
}