Adaptive Gait Generation for Multi-Terrain Exoskeletons via Constrained Kernelized Movement Primitives

2026-05-04

AI Summary

Existing lower-limb exoskeletons struggle to generate gaits in real time that are both environmentally adaptive and physiologically plausible across diverse terrains. This work proposes an adaptive gait generation framework based on kernelized movement primitives (KMP), which learns probabilistic gait models in both joint and task spaces from a small number of human demonstrations. By integrating environmental cues from an RGB-D camera, the approach formulates gait adaptation as a linearly constrained optimization problem with via-points. To the best of our knowledge, this is the first method to combine environmental perception with KMP for few-shot, multi-terrain adaptive gait planning. Simulations and real-world experiments on a physical exoskeleton demonstrate the method’s effectiveness, robustness, and kinematic feasibility across various scenarios, including level ground, slopes, stairs, and obstacle negotiation.

Abstract

Lower limb exoskeletons (LLEs) present the potential to make motor-impaired individuals walk again. Their application in real-world environments is still limited by the lack of effective adaptive gait planning. Indeed, current exoskeletons are meant to walk only on a flat and even terrain. Generating environment-aware, physiologically consistent gait trajectories in real-time is an open challenge. To overcome this, we propose a novel Kernelized Movement Primitives (KMP)-based framework for adaptive gait generation (AGG) across multiple indoor terrains. The proposed approach learns a probabilistic representation of human gait in both the joint and task spaces from a limited number of human demonstrations, representing natural gait characteristics and ensuring kinematic feasibility. In addition, the learned trajectories are adapted using environmental information extracted from an onboard RGB-D camera by treating the AGG as a linearly constrained optimization problem with via-points. The proposed method has been thoroughly validated first in simulations for gait generation in different scenarios, such as flat-ground walking, slopes, stairs, and obstacles crossing. Finally, the effectiveness and robustness of the method have been demonstrated with experiments on a commercial LLE in real-world scenarios. The results obtained demonstrate the feasibility of an environment-aware gait planning system for a new generation of intelligent lower limb exoskeletons for assisting people with disabilities in their every-day life.

Problem

• adaptive gait generation
• lower limb exoskeletons
• multi-terrain locomotion
• environment-aware planning
• gait trajectory

Innovation

• Adaptive Gait Generation
• Kernelized Movement Primitives
• Multi-Terrain Exoskeletons
• Environment-Aware Planning
• Constrained Optimization