Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis

2025-08-13

AI Summary

To address the challenge of achieving robust, reactive bipedal locomotion for exoskeleton robots in dynamic environments, this paper proposes a hybrid data-driven predictive control framework. The method innovatively embeds inter-step transition modeling into model predictive control (MPC), enabling unified optimization of discrete contact sequences and continuous motion trajectories while supporting online replanning. By representing system dynamics via Hankel matrices, it integrates data-driven predictive control with a step-to-step (S2S) state transition model, synthesizing motion and responding to real-time disturbances using only historical input–output data—without requiring explicit dynamical models. Experimental validation on the Atalante exoskeleton platform demonstrates significant improvements in walking robustness and environmental adaptability, enabling stable bipedal gait under complex, time-varying conditions.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Robust bipedal locomotion in exoskeletons requires the ability to dynamically react to changes in the environment in real time. This paper introduces the hybrid data-driven predictive control (HDDPC) framework, an extension of the data-enabled predictive control, that addresses these challenges by simultaneously planning foot contact schedules and continuous domain trajectories. The proposed framework utilizes a Hankel matrix-based representation to model system dynamics, incorporating step-to-step (S2S) transitions to enhance adaptability in dynamic environments. By integrating contact scheduling with trajectory planning, the framework offers an efficient, unified solution for locomotion motion synthesis that enables robust and reactive walking through online replanning. We validate the approach on the Atalante exoskeleton, demonstrating improved robustness and adaptability.

Problem

• Enables robust exoskeleton locomotion in dynamic environments
• Integrates foot contact scheduling with trajectory planning
• Improves adaptability through online motion replanning

Innovation

• Hybrid data-driven predictive control framework
• Hankel matrix-based dynamics modeling
• Integrated contact and trajectory planning