用分层模型实现高维人体模型的零样本实时运动控制
Motion Control of High-Dimensional Musculoskeletal Systems with Hierarchical Model-Based Planning
- 分层架构：模型预测控制规划目标姿态，形态感知比例控制协调动作
- 在多种任务中实现稳定运动，支持站立、不同地形行走和模仿体育动作
- 黑箱优化自动调奖，大幅减少人工调参需求
高维非线性系统（如生物与机器人系统）的控制因状态空间和动作空间巨大而困难。尽管深度强化学习在此类问题上取得成功，但计算成本高且耗时，难以应对需大量人工调参的大规模任务集合。本文提出层级式基于模型的学习算法MPC^2，用于高维复杂动力系统的零样本、近实时控制。MPC^2采用基于采样的模型预测控制器进行目标姿态规划，并通过形态感知的比例控制器实现执行器协同，提升控制鲁棒性。该算法可实现高维人体肌肉骨骼模型在多种运动任务中的运动控制，包括站立、不同地形行走及模仿体育动作。其奖励函数可通过黑箱优化自动调整，显著降低人工奖励工程的工作量。
原文摘要 · Abstract (English)
Controlling high-dimensional nonlinear systems, such as those found in biological and robotic applications, is challenging due to large state and action spaces. While deep reinforcement learning has achieved a number of successes in these domains, it is computationally intensive and time consuming, and therefore not suitable for solving large collections of tasks that require significant manual tuning. In this work, we introduce Model Predictive Control with Morphology-aware Proportional Control (MPC^2), a hierarchical model-based learning algorithm for zero-shot and near-real-time control of high-dimensional complex dynamical systems. MPC^2 uses a sampling-based model predictive controller for target posture planning, and enables robust control for high-dimensional tasks by incorporating a morphology-aware proportional controller for actuator coordination. The algorithm enables motion control of a high-dimensional human musculoskeletal model in a variety of motion tasks, such as standing, walking on different terrains, and imitating sports activities. The reward function of MPC^2 can be tuned via black-box optimization, drastically reducing the need for human-intensive reward engineering.
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