外观
论文arXiv 2610.0844122 天前
使用密度函数的安全多机器人协同运输
提出分层密度MPC，使多台四足机器人仅凭初始与目标位姿在MuJoCo中安全推送共享物体。
宇树 Go2原文链接：Safe Multi-Robot Collaborative Transport Using Density Functions（arXiv 2610.08441 · 发布日期：2026-10-06）
Safe Multi-Robot Collaborative Transport Using Density Functions
提出分层密度MPC，使多台四足机器人仅凭初始与目标位姿在MuJoCo中安全推送共享物体。
核心要点
- 面向多四足机器人协同推送共享物体，避免依赖预计算参考轨迹。
- 集中箱体级MPC优化接触力，并用控制密度约束实现目标收敛与避障。
- 各机器人在共享信息假设下运行分布式全身MPC，跟踪接触点并考虑障碍和邻居。
- 在MuJoCo中较CBF和RRT*跟踪基线显示有效性，论文提供实现视频。
验证范围与局限：验证限于MuJoCo仿真中两台和三台Unitree Go2推送刚体通过狭窄通道。
英文摘要 (Abstract)
This paper presents a hierarchical density-based model predictive control framework for safe collaborative manipulation by multiple quadrupedal robots. The framework enables a team of robots to push a shared object to a desired pose using only the initial and goal poses, without requiring a precomputed reference trajectory. A centralized box-level MPC optimizes contact forces while enforcing a control-density constraint for goal convergence and obstacle avoidance. Each robot then solves its own distributed robot-level whole-body MPC, under a stated shared-information assumption, to track its moving contact location while accounting for static obstacles and the time-varying positions of neighboring robots. The approach is evaluated in MuJoCo using whole-body contact dynamics for two and three Unitree Go2 quadrupeds collaboratively pushing rigid objects through narrow passages. Comparisons with matched Control Barrier Function and RRT* based tracking baselines demonstrate the effectiveness of the proposed density-based formulation for push-only, force- and torque-coupled manipulation tasks. Implementation videos are available at https://jaggu2606.github.io/go2-density-mpc-pushing/
作者：Jagannath Prasad Sahoo, Sriram S. K. S Narayanan, Umesh Vaidya
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