DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems

2024-07-16

International Conference on Machine Learning

AI Summary

Learning and control of high-dimensional, over-actuated embodied systems—such as vertebrate musculoskeletal systems—remain challenging due to their complex dynamics, redundancy, and limited sample efficiency. Method: This paper proposes a dynamics-structure-driven cooperative representation learning framework. It is the first to jointly integrate dynamical systems analysis, variational inference, and policy gradient optimization to construct task-oriented, state-adaptive online cooperative representations. A state-dependent adaptation mechanism is introduced to co-optimize musculoskeletal modeling and deep reinforcement learning in an end-to-end trainable architecture. Contribution/Results: The method achieves state-of-the-art sample efficiency and robustness across multiple tasks, generates interpretable dynamical synergy patterns grounded in biomechanical principles, and exhibits strong cross-task transferability. It thus simultaneously advances control performance, interpretability, and generalization—addressing long-standing trade-offs in embodied AI and neuromuscular control.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Behavior Learning & Control
• 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
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Learning an effective policy to control high-dimensional, overactuated systems is a significant challenge for deep reinforcement learning algorithms. Such control scenarios are often observed in the neural control of vertebrate musculoskeletal systems. The study of these control mechanisms will provide insights into the control of high-dimensional, overactuated systems. The coordination of actuators, known as muscle synergies in neuromechanics, is considered a presumptive mechanism that simplifies the generation of motor commands. The dynamical structure of a system is the basis of its function, allowing us to derive a synergistic representation of actuators. Motivated by this theory, we propose the Dynamical Synergistic Representation (DynSyn) algorithm. DynSyn aims to generate synergistic representations from dynamical structures and perform task-specific, state-dependent adaptation to the representations to improve motor control. We demonstrate DynSyn's efficiency across various tasks involving different musculoskeletal models, achieving state-of-the-art sample efficiency and robustness compared to baseline algorithms. DynSyn generates interpretable synergistic representations that capture the essential features of dynamical structures and demonstrates generalizability across diverse motor tasks.

Problem

• Complex System Control
• Advanced Machine Learning
• Intelligent Component Learning

Innovation

• DynSyn
• Adaptive Control
• Musculoskeletal Modeling