Hello Vaibhav,

thanks for the update.

> # 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

_______________________________________________
mlpack mailing list
mlpack@lists.mlpack.org
http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack

Reply via email to