Initialization of latent space coordinates via random linear projections for learning robotic sensory-motor sequences

Area of Science:

• Robotics
• Machine Learning
• Data Science

Background:

• Robot kinematic data is high-dimensional but exhibits high correlations within motion primitives.
• These correlations suggest motions can be modeled as points near low-dimensional affine subspaces.

Purpose of the Study:

• To investigate if random linear projection can effectively reduce dimensionality of robot kinematic data.
• To determine if projected data improves generative models for robot sensory-motor behavior primitives.

Main Methods:

• Applied random linear projection to robot motor sequences.
• Trained a Recurrent Neural Network (RNN) on projected kinematic data for a 9-DOF robotic manipulator.
• Initialized latent variables using projected data, zero values, and random values.

Main Results:

• Random linear projection retains significant information about the kinematic data structure.
• Initialization with projected data led to substantial improvements in RNN generalization for unseen samples.
• The learned latent space showed clear separation between different motor primitives from the start of training.

Conclusions:

• Random linear projection is an effective technique for dimensionality reduction of robot kinematic data.
• This method enhances the performance and interpretability of generative models for robot motor control.
• The approach facilitates the development of more robust and generalizable robot learning systems.