仅靠局部感知，模块化立方体机器人就能高效重构目标形状。
Reconfiguration of pivoting cube ensembles under local sensing constraints using geometric deep learning
- 每个立方体只通过邻近伙伴获取信息，用强化学习训练神经网络控制
- 仅需最近邻交互，通过多次信息传递即可实现接近最优的重构速度
- 适合空间系统、滑动模块机器人等需要分布式控制的场景
我们证明，在二维空间中，仅依赖局部感知即可实现同质旋转立方体模块机器人群体的有效全局重构。虽然立方体的选择（即哪个立方体执行动作）由全局协调，但每个立方体均由仅接收邻域信息的神经网络控制，并通过强化学习训练。此外，我们研究了在神经网络架构中引入立方体群组网格对称性（旋转与镜像）的影响。结果显示，即使最局部化的版本也能成功重构为目标形状，且个体可获得的全局信息越多，重构越快。通过多轮立方体间信息传递，仅使用最近邻交互即可实现近似最优重构。相较于标准神经网络架构，引入网格对称性虽对训练帮助有限，但能显著减小模型规模。该方法可迁移至其他空间相关系统，如滑动立方体模块机器人和CubeSat集群。
原文摘要 · Abstract (English)
We demonstrate that local sensing is sufficient for effective global reconfiguration of homogeneous pivoting cube modular robots in two dimensions. While cube selection (i.e., which cube executes a movement) is assumed to be globally coordinated, each cube in the ensemble is controlled by a neural network that only gains information from other cubes in its local neighbourhood, trained using reinforcement learning. Furthermore, we study the effect of including grid symmetries of the cube ensemble (rotation and mirroring) in the neural network architecture. We find that even the most localised versions succeed in reconfiguring to the target shape, although reconfiguration happens faster the more information about the whole ensemble is available to individual cubes. Near-optimal reconfiguration is achieved with only nearest neighbour interactions by using multiple information passing between cubes, allowing them to accumulate more global information about the ensemble. Compared to standard neural network architectures, including grid symmetries provides only minor benefits during training, but allows for reduced model sizes. The presented approach is transferable to other space-relevant systems with different action spaces, such as sliding cube modular robots and CubeSat swarms.
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