Real-time trajectory adaptation for quadrupedal locomotion using deep reinforcement learning

We present a control architecture for real-time adaptation and tracking of trajectories generated using a terrain-aware trajectory optimization solver. This approach enables us to circumvent the computationally exhaustive task of online trajectory optimization, and further introduces a control solut...

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Detalhes bibliográficos
Principais autores: Gangapurwala, S, Geisert, M, Orsolino, R, Fallon, M, Havoutis, I
Formato: Conference item
Idioma:English
Publicado em: IEEE 2021