I am trying to train the robot for specific actions such as grasping or pointing by using the RNN.
The robot is composed of one arm and a head containing camera in it. Also the workspace will be the small table so that the arm and objects can be located.
The input of the recurrent neural network wiil be the image frame of every time steps from the camera and the output will be the target motor angle of next frame of the robot arm.
When the current image frame is fed to the network, the network outputs the motor value of arm for the next frame. And when the arm reaches the next position, the input frame in that position is again goes to the network and it again yields the next motor output.
However, when making the data for training, I have to make all the data of (image, motor angle) pair for all the position on the workspace. Eventhough the network can do some generalization job by itselt, the data needed is stil too much and it takes lots of time since there are too many trajectories.
Generalizing the problem I have, the time for getting training data for network is too much. Is there any way or method that can train network with small size dataset? Or making huge dataset within relatively small human intervention?
Your question is very broad and definitely encompasses more than field of study. This question cannot be answered in this platform, however, i suggest you to check out this compilation of Machine Learning Resources on gitHub, specifically Data Analysis section.
A more specific resource related to your question is DeepNeuralClassifier.
I searched more paper and I found some that are related to the subject. The main topic of my question was to
find the way to train the network efficiently with small size of dataset
find the way to make huge dataset with small human effort
There were some papers and two of them helped me a lot. This is the link.
Explanation-Based Neural Network Learning for Robot Control
Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours
Related
For example,
I want to create an AI that plays Ticktacktoe, this is how I would go about it.
I have 9 input nodes which is for each space on the board, 3 nodes for one hidden layer (which I'm guessing would somehow benefit the AI by having it select a row or column with 3 spaces), and then 9 output nodes to see where the AI would put its mark on the entire board.
I'm lost on how I would find the cost of this neural network because I don't know how I would judge its prediction and affect its weights and biases.
If I wanted the AI to play a guessing game, it would make sense since I have the correct answer and I can teach it to be more accurate based on how off it was to the actual answer.
(NOTE: I am very new to neural networks, so there may be a simple answer that I've missed somewhere)
So, I did some digging around and found a good introduction to reinforcement learning. This is the method that is used to train neural networks to achieve a goal without knowing an exact target like which move is good in a certain scenario. Backpropagation is not the only learning method, but so many sources only used this method without letting the viewer know of any other methods which confused me.
Going through this playlist right now: https://www.youtube.com/watch?v=2pWv7GOvuf0&index=1&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT
Hope this will help someone getting started with neural networks!
I need to do dimensionality reduction from a series of images. More specifically, each image is a snapshot of a ball moving and the optimal features would be its position and velocity. As far as I know, CNN are the state-of-the-art for reducing the features for image classification, but in that case only a single frame is provided. Is it possible to extract also time-dependent features given many images at different time steps? Otherwise which is the state-of-the-art techniques for doing so?
It's the first time I use CNN and I would also appreciate any reference or any other suggestion.
If you want to be able to have the network somehow recognize a progression which is time dependent, you should probably look into recurrent neural nets (RNN). Since you would be operating on video, you should look into recurrent convolutional neural nets (RCNN) such as in: http://jmlr.org/proceedings/papers/v32/pinheiro14.pdf
Recurrence adds some memory of a previous state of the input data. See this good explanation by Karpathy: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
In your case you need to have the recurrence across multiple images instead of just within one image. It would seem like the first problem you need to solve is the image segmentation problem (being able to pick the ball out of the rest of the image) and the first paper linked above deals with segmentation. (then again, maybe you're trying to take advantage of the movement in order to identify the moving object?)
Here's another thought: perhaps you could only look at differences between sequential frames and use that as your input data to your convnet? The input "image" would then show where the moving object was in the previous frame and where it is in the current one. Larger differences would indicate larger amounts of movement. That would probably have a similar effect to using a recurrent network.
I have a question regarding the Matlab NN toolbox. As a part of research project I decided to create a Matlab script that uses the NN toolbox for some fitting solutions.
I have a data stream that is being loaded to my system. The Input data consists of 5 input channels and 1 output channel. I train my data on on this configurations for a while and try to fit the the output (for a certain period of time) as new data streams in. I retrain my network constantly to keep it updated.
So far everything works fine, but after a certain period of time the results get bad and do not represent the desired output. I really can't explain why this happens, but i could imagine that there must be some kind of memory issue, since as the data set is still small, everything is ok.
Only when it gets bigger the quality of the simulation drops down. Is there something as a memory which gets full, or is the bad sim just a result of the huge data sets? I'm a beginner with this tool and will really appreciate your feedback. Best Regards and thanks in advance!
Please elaborate on your method of retraining with new data. Do you run further iterations? What do you consider as "time"? Do you mean epochs?
At a first glance, assuming time means epochs, I would say that you're overfitting the data. Neural Networks are supposed to be trained for a limited number of epochs with early stopping. You could try regularization, different gradient descent methods (if you're using a GD method), GD momentum. Also depending on the values of your first few training datasets, you may have trained your data using an incorrect normalization range. You should check these issues out if my assumptions are correct.
I am trying to make a neural network for approximation of some unkown function (for my neural network course). The problem is that this function has very many variables but many of them are not important (for example in [f(x,y,z) = x+y] z is not important). How could I design (and learn) network for this kind of problem?
To be more specific the function is an evaluation function for some board game with unkown rules and I need to somehow learn this rules by experience of the agent. After each move the score is given to the agent so actually it needs to find how to get max score.
I tried to pass the neighborhood of the agent to the network but there are too many variables which are not important for the score and agent is finding very local solutions.
If you have a sufficient amount of data, your ANN should be able to ignore the noisy inputs. You also may want to try other learning approaches like scaled conjugate gradient or simple heuristics like momentum or early stopping so your ANN isn't over learning the training data.
If you think there may be multiple, local solutions, and you think you can get enough training data, then you could try a "mixture of experts" approach. If you go with a mixture of experts, you should use ANNs that are too "small" to solve the entire problem to force it to use multiple experts.
So, you are given a set of states and actions and your target values are the score after the action is applied to the state? If this problem gets any hairier, it will sound like a reinforcement learning problem.
Does this game have discrete actions? Does it have a discrete state space? If so, maybe a decision tree would be worth trying?
I'm fairly new to MATLAB, but have acquainted myself with Simulink and Computer Vision over the past few days. My problem statement involves taking a traffic/highway video input and detecting if an accident has occurred.
I plan to do this by extracting the values of centroid to plot trajectory, velocity difference (between frames) and distance between two vehicles. I can successfully track the centroids, and aim to derive the rest of the features.
What I don't know is how to map these to ANN. I mean, every image has more than one vehicle blobs, which means, there are multiple centroids in a single frame/image. So, how does NN act on multiple inputs (the extracted features per vehicle) simultaneously? I am obviously missing the link. Help me figure it out please.
Also, am I looking at time series data?
I am not exactly sure about your question. The problem can be both time series data and not. You might be able to transform the time series version of the problem, such that it can be solved using ANN, but it is sort of a Maslow's hammer :). Also, Could you rephrase the problem.
As you said, you could give it features from two or three frames and then use the classifier to detect accident or not, but it might be difficult to train such a classifier. The problem is really difficult and the so you might need tons of training samples to get it right, esp really good negative samples (for examples cars travelling close to each other) etc.
There are multiple ways you can try to solve this problem of accident detection. For example : Build a classifier (ANN/SVM etc) to detect accidents without time series data. In which case your input would be accident images and non accident images or some sort of positive and negative samples for training and later images for test. In this specific case, you are not looking at the time series data. But here you might need lots of features to detect the same (this in some sense a single frame version of the problem).
The second method would be to use time series data, in which case you will have to detect the features, track the features (say using Lucas Kanade/Horn and Schunck) and then use the information about velocity and centroid to detect the accident. You might even be able to formulate it for HMMs.