Estimating Forces of Robotic Pouring Using a LSTM RNN
This work addresses the need for robots to estimate system dynamics, but it is incremental as it applies an existing LSTM method to a specific robotic pouring scenario.
The paper tackled the problem of estimating dynamics from sequences of input data for robotic pouring by using an LSTM recurrent neural network to predict one output state from nine input states, achieving results that demonstrate the network's capability for such estimation tasks.
In machine learning, it is very important for a robot to be able to estimate dynamics from sequences of input data. This problem can be solved using a recurrent neural network. In this paper, we will discuss the preprocessing of 10 states of the dataset, then the use of a LSTM recurrent neural network to estimate one output state (dynamics) from the other 9 input states. We will discuss the architecture of the recurrent neural network, the data collection and preprocessing, the loss function, the results of the test data, and the discussion of changes that could improve the network. The results of this paper will be used for artificial intelligence research and identify the capabilities of a LSTM recurrent neural network architecture to estimate dynamics of a system.