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Luckeciano M. created project Deep Reinforcement Learning for Humanoid Robot Walking and Kicking

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Deep Reinforcement Learning for Humanoid Robot Walking and Kicking

In this project, we apply modern Deep RL algorithms for optimizing Walking Engine and Kicking in Soccer 3D Simulation. The parameter optimization of walking and kicking is a big challenge in Robocup Soccer 3D Simulation enviroment. Until last year, the walking and kicking parameters are optimized by CMA-ES algorithm (a variant of genetic algorithms). This algorithm does not scale for hundreds or thousands of parameters, and it's proved that the bigger the number of parameters, the better is kicking and walking. Now, the Soccer 3D community started to apply Neural Networks for model the kick engine, and the results are very impressive. Due this, we started a project for finding better architectures for model the kick and started to apply the same techniques in walking engine. We propose to learn the entire walking engine with these techniques, replacing the traditional Double Inverted Pendulum-based walking engine.

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Luckeciano M. posted an update in Student Developers for AI

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Luckeciano Melo

Hi Everyone!

My name is Luckeciano Melo. I'm 21 years old and I'm from Brazil. I am student of BSc in Computer Engineering and MSc of Deep Learning in ITA (Aeronautics Institute of Technology), one of the most prestigious engineering school in the country.

I work with Machine Perception and Robotics. I've worked with Computer Vision for Facial Recognition using Deep ConvNets and nowadays I'm starting to work with Automatic Speech Recognition using end-to-end Deep Learning approaches. For my final work of BSc and Master's Thesis, I'm applying Deep Reinforcement Learning for humanoid walking and kick in Robocup Soccer 3D Simulation enviroment.

My main interest is Artificial Intelligence, as stated above. Furthermore, I also have experience in Software Engineering. I'm more experienced in C/C++ and Python.

If you are interested in my project you can reach me on github ( or LinkedIn.


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