Feature Space Decomposition for effective robot adaptation

Adaptation is an essential capability for intelligent robots to work in new environments. In the learning framework of Programming by Demonstration (PbD) and Reinforcement Learning (RL), a robot usually learns skills from a latent feature space obtained by dimension reduction techniques. Because the latent space is optimized for a specific environment during the training phase, it typically contains fewer variations. Accordingly, searching for a solution within the latent space can be less effective for robot adaptation to new environments with unseen changes. In this paper, we propose a novel Feature Space Decomposition (FSD) approach to effectively address the robot adaptation problem, which is directly applicable to the learning framework based on PbD and RL. Our FSD method decomposes the high-dimensional original features extracted from the demonstration data into principal and non-principal feature space. Then, the non-principal features are used to form a new low-dimensional search space for autonomous robot adaptation based on RL, which is initialized using a generalized trajectory represented by a Gaussian Mixture Model that is learned from the principal features. The scalability of our FSD approach guarantees that optimal solutions can be found in the new non-principal space, if they exist in the original feature space. Experimental results on real robots validate that our FSD approach enables the robots to effectively adapt to new environments, and is usually able to find optimal solutions more quickly than traditional approaches when significant environment changes occur.