模块化机器人电机控制中的通信：现实约束下的双边控制器
Communication in modular robotic motor control: Bilateral controllers under realistic constraints
- Monash University（莫纳什大学）
- Tel Aviv University（特拉维夫大学）
- Cerenaut（瑟雷诺特公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出受大脑双侧结构启发的模块化GRU双边控制器，在双臂肌肉骨骼仿真任务中，其性能优于整体架构及无通信的模块化控制器，证明模块间通信可优化机器人运动的精度、成本与稳定性。
AI中文摘要：
肌肉骨骼系统中的机器人电机控制需要快速、精准的运动以及在信号相关噪声（电机命令方差随命令幅度缩放）和能量成本下的稳健姿态稳定。模块化控制器可将这些相互冲突的需求分配到交互子模块中，但尚不清楚它们在现实约束下是否优于整体架构，以及模块间通信如何塑造最终策略。受大脑双侧半球组织的启发，我们引入了一种由两个基于GRU的模块组成的循环控制器，模块间通过可学习的延迟半球间通道连接，在可微分的双臂肌肉骨骼模拟器中进行端到端训练。在伸手和保持任务中，该模块化架构显著优于容量匹配的整体基线。与无通信的匹配模块化控制器相比，学习到的半球间通信改变了解决方案：端点精度提升，非零延迟状态下的能量成本降低，肌肉协同收缩减少。我们的发现表明，对于机器人技术而言，受生物启发的模块化控制器为在噪声和能量约束下实现稳健运动提供了实用途径，模块间通信为调节精度、稳定性和驱动成本之间的权衡提供了机制。
英文摘要：
Robotic motor control in musculoskeletal systems requires fast, accurate movement and robust postural stabilization under signal-dependent noise (where motor command variance scales with command magnitude) and energetic cost. Modular controllers can distribute these competing demands across interacting submodules, but it remains unclear whether they outperform monolithic architectures under realistic constraints, and how inter-module communication shapes the resulting strategy. Inspired by the bilateral hemispheric organization of the brain, we introduce a recurrent controller of two GRU-based modules connected by a learnable, delayed inter-hemispheric channel, trained end-to-end in a differentiable two-arm musculoskeletal simulator. Across reaching and holding tasks, the modular architecture substantially outperforms a capacity-matched monolithic baseline. Compared to a matched modular controller without communication, learned inter-hemispheric communication reshapes the solution: improved endpoint precision, lower energetic cost in non-zero-delay regimes, and reduced muscle co-contraction. Our findings show that for robotics, biologically inspired modular controllers offer a practical route to robust movement under noise and energetic constraints, with inter-module communication providing a mechanism to tune trade-offs between precision, stability, and actuation cost.
