DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning (Presentation) 

Thanks, we are glad you enjoyed it. During deployment we do not need to re-sample until it's valid, we only need a compute the mean of the policy's distribution to generate phase plans. That's the point of formulating the MDP in order to train the policy with RL; instead of using it like in sampling-based-planning methods, we train the parameterized policy distribution with RL so it learns to always output valid phase transitions.

@@leggedrobotics Thanks, understood.

@@leggedrobotics Hi, Is it possible to fast forward the learning process so that the robot can spend 1 million years learning in only a few weeks?

This visualization was made in raisimOgre so unfortunately there is no easy-to-use configuration to share. Stay tuned for when we release the code though.

This work uses the RaiSim physics engine that was developed in-house. Link: raisim.com/

Unfortunately not yet. We do plan to open-source the code later this year though.

@@leggedrobotics that shall be great contribution!

@@leggedrobotics what is the constraint for the speed at which It walks? Does it have to go at that speed or that as fast as possible?