用于组合运动控制的适配器库选项学习
Learning Options for Compositional Motor Control with Adapter Banks
- Columbia University（哥伦比亚大学）
- New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一种共享循环核心与残差适配器库结合的架构，通过离散潜在代码选择适配器，在闭环控制中学习低秩运动基元，实现分布外运动泛化，显著优于多任务基线。
AI中文摘要：
学习灵活的运动基元是熟练运动控制的标志。最近的神经科学理论提出，运动基元可能通过共享循环网络的低秩扰动来实现，但未阐明此类系统如何被学习。我们将这一原理转化为一种用于端到端学习运动技能的新颖架构：一个由残差适配器库调制的共享循环核心，每个适配器由离散潜在代码选择。在闭环生物力学控制上训练后，适配器在无架构秩约束的情况下发展出循环动力学的涌现低秩扰动，将任务表示置于共享核心网络的不同子空间中。一个简单的高层策略基于学习到的选项，在整个网络冻结时进行优化，对低秩适配器进行排序以产生新颖的分布外运动。我们展示了在闭环控制设置中泛化到新颖运动序列的能力，将任务输入条件的多任务基线的泛化误差提高了最多一个数量级。
英文摘要：
Learning flexible motor primitives is a hallmark of skilled motor control. Recent neuroscience theory proposes that motor primitives may be implemented as low-rank perturbations of a shared recurrent network, but leaves open how such a system is learned. We translate this principle into a novel architecture for learning motor skills end-to-end: a shared recurrent core modulated by a bank of residual adapters, each selected by a discrete latent code. Trained on closed-loop biomechanical control, the adapters develop emergent low-rank perturbations of the recurrent dynamics despite no architectural rank constraint, placing task representations in disparate subspaces of the shared core network. A simple high-level policy over the learned options, optimized while the whole network is frozen, sequences the low-rank adapters to produce novel out-of-distribution movements. We demonstrate the ability to generalize to novel motor sequences within the closed-loop control setting, improving on the generalization error of a task-input-conditioned multitask baseline by upto order of magnitude.
