We present DisCo, a distributed algorithm for contact-rich, multi-robot tasks. DisCo is a distributed contact-implicit trajectory optimization algorithm, which allows a group of robots to optimize a time sequence of forces to objects and to their environment to accomplish tasks such as collaborative manipulation, robot team sports, and modular robot locomotion. We build our algorithm on a variant of the Alternating Direction Method of Multipliers (ADMM), where each robot computes its own contact forces and contact-switching events from a smaller single-robot, contact-implicit trajectory optimization problem, while cooperating with other robots through dual variables, enforcing constraints between robots. Each robot iterates between solving its local problem, and communicating over a wireless mesh network to enforce these consistency constraints with its neighbors, ultimately converging to a coordinated plan for the group. The local problems solved by each robot are significantly less challenging than a centralized problem with all robots' contact forces and switching events, improving the computational efficiency, while also preserving the privacy of some aspects of each robot's operation. We demonstrate the effectiveness of our algorithm in simulations of collaborative manipulation, multi-robot team sports scenarios, and in modular robot locomotion, where DisCo achieves $3$x higher success rates with a 2.5x to 5x faster computation time. Further, we provide results of hardware experiments on a modular truss robot, with three collaborating truss nodes planning individually while working together to produce a punctuated rolling-gate motion of the composite structure. Videos are available on the project page: https://disco-opt.github.io.

翻译：本文提出DisCo，一种用于接触密集型多机器人任务的分布式算法。DisCo是一种分布式接触隐式轨迹优化算法，它使得机器人群体能够优化对物体及环境的作用力时序，以完成协作操控、机器人团队运动以及模块化机器人移动等任务。该算法基于交替方向乘子法（ADMM）的变体构建，其中每个机器人通过求解规模较小的单机器人接触隐式轨迹优化问题来计算自身的接触力与接触切换事件，同时通过与其他机器人的对偶变量协作来强制执行机器人间的约束条件。每个机器人在求解本地问题与通过无线网状网络通信以强化邻域一致性约束之间迭代计算，最终收敛至群体协调规划方案。相较于集中式优化所有机器人接触力与切换事件的复杂问题，各机器人求解的本地问题计算难度显著降低，在提升计算效率的同时，也保护了各机器人运行细节的隐私性。我们在协作操控、多机器人团队运动场景以及模块化机器人移动的仿真中验证了算法有效性，DisCo实现了成功率提升3倍，计算速度加快2.5至5倍。此外，我们在模块化桁架机器人硬件平台上进行了实验，三个协作桁架节点在独立规划的同时协同工作，实现了复合结构的间歇式滚动门运动。演示视频详见项目页面：https://disco-opt.github.io。