ami-iit/paper_romualdi_viceconte_2024_humanoids_dnn-mpc-walking
[Humanoids 2024 award finalist] Online DNN-Driven Nonlinear MPC for Stylistic Humanoid Robot Walking with Step Adjustment
This project helps robotics researchers and engineers test and evaluate advanced control algorithms for humanoid robots. It takes in various walking style parameters and outputs realistic humanoid robot movements in a simulated environment, allowing users to analyze and refine how robots walk with different gaits and step adjustments. The ideal user is someone involved in robotics research, particularly in bipedal locomotion and control.
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Use this if you are a robotics researcher or engineer looking to simulate and test advanced, stylistic walking gaits and step adjustments for humanoid robots using deep neural networks and model predictive control.
Not ideal if you are looking for a simple, out-of-the-box solution for basic robot movement, or if your focus is on hardware implementation without prior simulation and control algorithm development.
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BSD-3-Clause
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Last pushed
Feb 05, 2025
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