Multi-robot collaborative transportation is a critical capability that has attracted significant attention over recent years. To reliably transport a kinematically constrained payload, a team of robots must closely collaborate and coordinate their individual velocities to achieve the desired payload motion. For quadruped robots, a key challenge is caused by their anisotropic velocity limits, where forward and backward movement is faster and more stable than lateral motion. In order to enable dual-quadruped collaborative transportation and address the above challenges, we propose a novel Bilevel Learning for Collaborative Transportation (BLCT) approach. In the upper-level, BLCT learns a team collaboration policy for the two quadruped robots to move the payload to the goal position, while accounting for the kinematic constraints imposed by their connection to the payload. In the lower-level, BLCT optimizes velocity controls of each individual robot to closely follow the collaboration policy while satisfying the anisotropic velocity constraints and avoiding obstacles. Experiments demonstrate that our BLCT approach well enables collaborative transportation in challenging scenarios and outperforms baseline approaches.

翻译：多机器人协作运输是一项近年来备受关注的关键能力。为可靠地运输受运动学约束的载荷，机器人团队必须紧密协作并协调各自的运动速度，以实现期望的载荷运动。对于四足机器人而言，其主要挑战源于其各向异性速度限制——前后移动比侧向运动更快且更稳定。为实现双四足机器人协作运输并解决上述挑战，我们提出了一种新颖的协作运输双层学习（BLCT）方法。在上层，BLCT学习双四足机器人的团队协作策略，使载荷运动至目标位置，同时考虑机器人连接载荷所施加的运动学约束。在下层，BLCT优化每个机器人的速度控制，使其紧密跟随协作策略，同时满足各向异性速度约束并避开障碍物。实验表明，我们的BLCT方法在复杂场景中能有效实现协作运输，其性能优于基线方法。