Hello Vaibhav, thanks for the update.

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> # First we need to find the required final coordinates of the object (in the > simulation we would already know that, but in the real world, we need to use > some 3D camera). > # Then we can find the optimal path using trajectory planning techniques > # After that we can find the joint angles using inverse kinematics > # Using these joint angles, we can train our model That is definitely one way, but perhaps we can simplify the way, e.g. by using only a single camera, this makes the overall training more challenging since, the preprocessing step is more complex; but at the end, you can directly use the pipeline for a broad range of applications, without extracting a lot of model- specific information. Let me know what you think. > I wanted to you ask how to train mlpack models on large datasets on cloud. It > would help me to create a detailed timeline for my proposal. Please let me > know > what you think. Do you mean distributed training? I think for this project we can start small, but we could we can use Downpour SGD to distribute the load. Thanks, Marcus > On 12. Mar 2018, at 09:18, Vaibhav Jain <vabs...@gmail.com> wrote: > > Hey Marcus, > I think this would be a good approach to work on this project: > # First we need to find the required final coordinates of the object (in the > simulation we would already know that, but in the real world, we need to use > some 3D camera). > # Then we can find the optimal path using trajectory planning techniques > # After that we can find the joint angles using inverse kinematics > # Using these joint angles, we can train our model > > For learning manipulator kinematics/dynamics, lectures by Prof. Oussama > Khatib at Stanford, are very good. The book "Introduction to Robotics: > Mechanics and Control" by J. Craig is also very good. > For simulation, the environments you suggested would be surely helpful. > Although, I need to work a little bit more to see the extent to which they > can be used. > > I wanted to you ask how to train mlpack models on large datasets on cloud. It > would help me to create a detailed timeline for my proposal. > Please let me know what you think. > > On Wed, Feb 28, 2018 at 6:22 PM, Marcus Edel <marcus.e...@fu-berlin.de > <mailto:marcus.e...@fu-berlin.de>> wrote: > Hello Vaibhav, > > OpenAI released a couple of robotics environments wich could be interesting: > https://github.com/openai/gym/tree/master/gym/envs/robotics > <https://github.com/openai/gym/tree/master/gym/envs/robotics> > > Best, > Marcus > >> On 27. Feb 2018, at 17:00, Vaibhav Jain <vabs...@gmail.com >> <mailto:vabs...@gmail.com>> wrote: >> >> Hello Marcus, >> Thanks for this resource. I will sure check it out. In the meantime, I will >> also research some other previous similar projects. >> >> >> On Feb 27, 2018 03:36, "Marcus Edel" <marcus.e...@fu-berlin.de >> <mailto:marcus.e...@fu-berlin.de>> wrote: >> Hello Vaibhav, >> >> thanks for the input, I agree ROS is definitely a good option to get started, >> the available resources is just one point. My initial idea was to integrate >> the >> simulator in the OpenAI Gym framework, mlpack already follows the gym >> interface >> so the interaction is already in place, but I think we could use the Gazebo >> extension for Gym to get it working. This looks promising >> (https://github.com/erlerobot/gym-gazebo >> <https://github.com/erlerobot/gym-gazebo>). Not sure the install process is >> straightforward, in the best case we can install everything with a few clicks >> and most packages are available for a bunch of distributions. >> >> Thanks, >> Marcus >> >> __ >> Vaibhav Jain >> > > > > Regards, > -- > - Vaibhav Jain

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