Enhancing Robotic Adaptability: Integrating Unsupervised Trajectory Segmentation and Conditional ProMPs for Dynamic Learning Environments
Year: 2024
Type: conference-paper
Abstract: We propose a novel framework for enhancing robotic adaptability and learning efficiency, which integrates unsupervised trajectory segmentation with adaptive probabilistic movement primitives (ProMPs). By employing a cutting-edge deep learning architecture that combines autoencoders and Recurrent Neural Networks (RNNs), our approach autonomously pinpoints critical transitional points in continuous, unlabeled motion data, thus significantly reducing dependence on extensively labeled datasets. This innovative method dynamically adjusts motion trajectories using conditional variables, significantly enhancing the flexibility and accuracy of robotic actions under dynamic conditions while also reducing the computational overhead associated with traditional robotic programming methods. Our experimental validation demonstrates superior learning efficiency and adaptability compared to existing techniques, paving the way for advanced applications in industrial and service robotics.
Author: Tianci Gao
Institution: Bauman Moscow State Technical University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403678657