Learning hybrid locomotion skills-Learn to exploit residual actions and modulate model-based gait control

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Kasaei, M (Author) Other; Abreu, M (Author) Other; lau, n (Author) Other; Pereira, A (Author) Other; reis, lp (Author) FEUP; Li, ZB (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Front. Robotics and AI

Vol. 10

ISSN: 2296-9144

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-9RP

DOI: 10.3389/frobt.2023.1004490

Abstract (EN): This work has developed a hybrid framework that combines machine learning and control approaches for legged robots to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a model-based, full parametric closed-loop and analytical controller as the gait pattern generator. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel, and also generate compensatory actions for all joints, thus significantly augmenting the stability under unexpected perturbations. Seven Neural Network policies with different configurations were optimized to validate the effectiveness and the combined use of the modulation of the kernel parameters and the compensation for the arms and legs using residual actions. The results validated that modulating kernel parameters alongside the residual actions have improved the stability significantly. Furthermore, The performance of the proposed framework was evaluated across a set of challenging simulated scenarios, and demonstrated considerable improvements compared to the baseline in recovering from large external forces (up to 118%). Besides, regarding measurement noise and model inaccuracies, the robustness of the proposed framework has been assessed through simulations, which demonstrated the robustness in the presence of these uncertainties. Furthermore, the trained policies were validated across a set of unseen scenarios and showed the generalization to dynamic walking.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14