Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling

2024-05-02

AI Summary

To address the challenge of accurately replicating human coordinated locomotion for lower-limb amputees in dynamic environments, this paper proposes a continual learning framework tailored for biomimetic prostheses. Methodologically, it introduces a novel multi-task prospective rehearsal mechanism that jointly integrates temporal kinematic prediction with self-correcting feedback. A lightweight task-specific module architecture is co-designed with a shared backbone network to balance personalized modeling and cross-scenario generalization. The framework unifies continual learning, multi-task learning, demonstration-based imitation learning, adversarial robust training, and neural modularization. Evaluated on real-world gait datasets from amputee users, the model significantly outperforms baseline methods, maintaining high prediction accuracy and robustness under distribution shifts, sensor noise, and adversarial perturbations. This work establishes a scalable, adaptive paradigm for biomimetic prosthesis control.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Life-Long and Continual Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Lower limb amputations and neuromuscular impairments severely restrict mobility, necessitating advancements beyond conventional prosthetics. While motorized bionic limbs show promise, their effectiveness depends on replicating the dynamic coordination of human movement across diverse environments. In this paper, we introduce a model for human behavior in the context of bionic prosthesis control. Our approach leverages human locomotion demonstrations to learn the synergistic coupling of the lower limbs, enabling the prediction of the kinematic behavior of a missing limb during tasks such as walking, climbing inclines, and stairs. We propose a multitasking, continually adaptive model that anticipates and refines movements over time. At the core of our method is a technique called multitask prospective rehearsal, that anticipates and synthesizes future movements based on the previous prediction and employs a corrective mechanism for subsequent predictions. Our evolving architecture merges lightweight, task-specific modules on a shared backbone, ensuring both specificity and scalability. We validate our model through experiments on real-world human gait datasets, including transtibial amputees, across a wide range of locomotion tasks. Results demonstrate that our approach consistently outperforms baseline models, particularly in scenarios with distributional shifts, adversarial perturbations, and noise.

Problem

• Develops adaptive bionic limb movement prediction
• Enhances prosthetics via human locomotion simulation
• Addresses mobility in diverse environmental conditions

Innovation

• Multitask prospective rehearsal technique
• Synergistic lower limb coupling learning
• Lightweight task-specific modular architecture