  Imitation learning enables robots to learn and replicate human behavior from
training data. Recent advances in machine learning enable end-to-end learning
approaches that directly process high-dimensional observation data, such as
images. However, these approaches face a critical challenge when processing
data from multiple modalities, inadvertently ignoring data with a lower
correlation to the desired output, especially when using short sampling
periods. This paper presents a useful method to address this challenge, which
amplifies the influence of data with a relatively low correlation to the output
by inputting the data into each neural network layer. The proposed approach
effectively incorporates diverse data sources into the learning process.
Through experiments using a simple pick-and-place operation with raw images and
joint information as input, significant improvements in success rates are
demonstrated even when dealing with data from short sampling periods.
