多只机器狗通过接触力协作搬运物体，无需通信或集中控制。
Multi-Quadruped Cooperative Object Transport: Learning Decentralized Pinch-Lift-Move
- 分层策略分离移动与抓取控制，用奖励函数模拟刚性连接。
- 2到10只机器狗在多种形状质量物体上实现稳定搬运，成功率超90%。
- 适合大规模机器人协同任务，支持从仿真到现实的迁移应用。
我们研究使用N只带机械臂的四足机器人团队，仅通过物理接触完成夹持、抬升和移动无法抓握的物体，且不依赖机器人与物体之间的刚性连接。不同于以往需要机械耦合的方法，本工作解决更复杂的场景：各机器人独立运作，仅通过接触力协调，无通信或中央控制。为此，我们采用分层策略架构，将基座运动与机械臂控制解耦，并提出一种星群奖励机制，统一位置与姿态跟踪以强制刚性接触行为。核心思路是通过精心设计的奖励与训练流程，使机器人在行为上表现得像与物体刚性相连，而非依赖显式约束。该方法通过共享策略参数和隐式同步信号实现协调，可扩展至任意规模团队且无需重新训练。我们在大量仿真中验证了对2至10只机器人的有效运输能力，涵盖多样几何形态与质量的物体；并展示了轻量级物体在真实环境中的仿真到现实迁移结果。
原文摘要 · Abstract (English)
We study decentralized cooperative transport using teams of N-quadruped robots with arm that must pinch, lift, and move ungraspable objects through physical contact alone. Unlike prior work that relies on rigid mechanical coupling between robots and objects, we address the more challenging setting where mechanically independent robots must coordinate through contact forces alone without any communication or centralized control. To this end, we employ a hierarchical policy architecture that separates base locomotion from arm control, and propose a constellation reward formulation that unifies position and orientation tracking to enforce rigid contact behavior. The key insight is encouraging robots to behave as if rigidly connected to the object through careful reward design and training curriculum rather than explicit mechanical constraints. Our approach enables coordination through shared policy parameters and implicit synchronization cues - scaling to arbitrary team sizes without retraining. We show extensive simulation experiments to demonstrate robust transport across 2-10 robots on diverse object geometries and masses, along with sim2real transfer results on lightweight objects.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。