Payload transport over flat terrain via multi-wheel robot carriers is well-understood, highly effective, and configurable. In this paper, our goal is to provide similar effectiveness and configurability for transport over rough terrain that is more suitable for legs rather than wheels. For this purpose, we consider multi-biped robot carriers, where wheels are replaced by multiple bipedal robots attached to the carrier. Our main contribution is to design a decentralized controller for such systems that can be effectively applied to varying numbers and configurations of rigidly attached bipedal robots without retraining. We present a reinforcement learning approach for training the controller in simulation that supports transfer to the real world. Our experiments in simulation provide quantitative metrics showing the effectiveness of the approach over a wide variety of simulated transport scenarios. In addition, we demonstrate the controller in the real-world for systems composed of two and three Cassie robots. To our knowledge, this is the first example of a scalable multi-biped payload transport system.

翻译：在平坦地形上通过多轮机器人运输有效载荷的技术已得到充分理解，具有高效性和可配置性。本文的目标是，为更适合腿部而非轮式移动的崎岖地形运输任务，提供具备类似高效性和可配置性的解决方案。为此，我们考虑使用多足机器人运输系统，即用多个连接到载具的双足机器人替代轮子。我们的主要贡献是为此类系统设计了一种分散式控制器，该控制器无需重新训练即可有效应用于不同数量和配置的刚性连接双足机器人。我们提出了一种在仿真中训练控制器的强化学习方法，该方法支持向现实世界的迁移。我们在仿真中的实验提供了定量指标，证明了该方法在多种模拟运输场景中的有效性。此外，我们在现实世界中展示了由两个和三个Cassie机器人组成的系统对该控制器的应用。据我们所知，这是首个可扩展的多足机器人有效载荷运输系统的实例。