Enhancing Robotic Skill Acquisition Through Sparse Behavioral Data and Latent Space Exploration

Robotic skill acquisition often suffers from the high dimensionality and sparsity of behavioral data, hindering efficient learning. This paper presents a novel approach to address this challenge by leveraging representation learning techniques to extract meaningful latent representations from sparse robotic demonstrations. We propose a framework that combines Variational Autoencoders (VAEs) with a reinforcement learning policy optimized within the learned latent space. This allows the robot to generalize from limited data and explore novel behaviors more effectively. We demonstrate the efficacy of our approach in a simulated robotic manipulation task, showing improved performance and sample efficiency compared to traditional reinforcement learning methods.