Robotic collaborative carrying could greatly benefit human activities like warehouse and construction site management. However, coordinating the simultaneous motion of multiple robots represents a significant challenge. Existing works primarily focus on obstacle-free environments, making them unsuitable for most real-world applications. Works that account for obstacles, either overfit to a specific terrain configuration or rely on pre-recorded maps combined with path planners to compute collision-free trajectories. This work focuses on two quadrupedal robots mechanically connected to a carried object. We propose a Reinforcement Learning (RL)-based policy that enables tracking a commanded velocity direction while avoiding collisions with nearby obstacles using only onboard sensing, eliminating the need for precomputed trajectories and complete map knowledge. Our work presents a hierarchical architecture, where a perceptive high-level object-centric policy commands two pretrained locomotion policies. Additionally, we employ a game-inspired curriculum to increase the complexity of obstacles in the terrain progressively. We validate our approach on two quadrupedal robots connected to a bar via spherical joints, benchmarking it against optimization-based and decentralized RL baselines. Our hardware experiments demonstrate the ability of our system to locomote in unknown environments without the need for a map or a path planner. The video of our work is available in the multimedia material.

翻译：机器人协作搬运可极大提升仓库与建筑工地管理等人类活动的效率。然而，协调多台机器人同步运动仍是一项重大挑战。现有研究主要聚焦于无障碍环境，难以适用于多数现实场景。针对障碍物的研究要么过度适配特定地形构型，要么依赖预录地图配合路径规划器计算无碰撞轨迹。本工作研究两台通过机械连接与搬运物耦合的四足机器人。我们提出一种基于强化学习（RL）的策略，利用机载感知能力在追踪指令速度方向的同时规避附近障碍物，无需预计算轨迹或完整地图信息。本文提出一种分层架构：具有感知能力的高层级以物体为中心的策略控制两个预训练的步态策略。此外，我们采用游戏机制启发的课程学习逐步增加地形障碍复杂度。我们通过球形关节连接至横杆的两台四足机器人验证该方法，并与基于优化的基线方法和分散式强化学习基线方法进行对比。硬件实验表明，本系统无需地图或路径规划器即可在未知环境中运动。工作视频详见多媒体材料。