λ-保持控制：在预测性肌肉骨骼模拟中，从最小任务奖励中涌现出类人运动
Lambda-Hold Control: Human-Like Movement Emerges from a Minimal Task Reward in Predictive Musculoskeletal Simulation
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中文总结 AI 辅助
该研究提出基于EP假说的λ-保持控制器，整合相关控制方法，首次实现肌肉驱动骨骼模型用最小奖励在1小时内学习类人短跑运动，助力开发可学习的人类运动控制器模型。
中文摘要 AI 辅助
人类肌肉骨骼系统的过度驱动特性，使得通过强化学习训练肌肉骨骼模型生成类人运动极具挑战性，主要原因是由此产生的高维冗余动作空间中的探索效率极低。为解决该问题，我们提出λ-保持控制器，其灵感来自平衡点（EP）假说，该假说已得到人类运动控制研究的大量证据广泛支持。该策略的控制变量是每块肌肉的EP阈值长度λ，拉伸反射募集定律会据此自动计算肌肉激活度。在步态相位区间内保持每个λ，还能大幅降低策略被查询的频率。因此，该控制器据我们所知首次实现了肌肉驱动骨骼模型仅用最小奖励在一小时训练内学习类人短跑运动。通过所提λ-保持控制器的高效探索不仅是工程技巧，更是基于生理学的方法，整合了EP假说、间歇控制和最优反馈控制。除了在预测模拟中封装类人行为外，该成果还为开发可学习的人类运动控制器模型做出了贡献。
英文摘要
The massive overactuation in the human musculoskeletal system makes it challenging to train musculoskeletal models to generate human-like motion via reinforcement learning, primarily because exploration in the resulting high-dimensional and redundant action space is extremely inefficient. To address this problem, we propose the $λ$-hold controller, inspired by the equilibrium-point (EP) hypothesis, which has been widely supported by extensive evidence from human motor control studies. The policy's control variable is the per-muscle EP threshold length $λ$, from which a stretch-reflex recruitment law computes the muscle excitations automatically. Holding each $λ$ over an interval of the gait phase also sharply reduces the frequency at which the policy must be queried. Consequently, the controller, to our knowledge for the first time, enables a muscle-actuated skeletal model to learn human-like sprinting using only a minimal reward within an hour of training. The efficient exploration through the proposed $λ$-hold controller is not merely an engineering trick but an approach grounded in physiology, bringing together the EP hypothesis, intermittent control, and optimal feedback control. Beyond encapsulating human-like behavior in predictive simulation, this achievement contributes to developing a learnable model of the human motor controller.
发表机构
- Institute of Sport Science, Seoul National University（首尔国立大学体育科学研究所）
- SNU Robotics Institute, Seoul National University（首尔国立大学SNU机器人研究所）
- Department of Physical Education, Seoul National University（首尔国立大学体育学院）
机构由 AI 辅助整理，请以论文原文为准。
