This paper proposes a task-oriented model predictive control (ToMPC) framework for safe and efficient robotic manipulation in open workspaces. The framework unifies collision-free motion and robot-environment interaction to address diverse scenarios. Additionally, it introduces task-oriented obstacle avoidance that leverages kinematic redundancy to enhance manipulation efficiency in obstructed environments. This complex optimization problem is solved by the alternating direction method of multipliers (ADMM), which decomposes the problem into two subproblems tackled by differential dynamic programming (DDP) and quadratic programming (QP), respectively. The effectiveness of this approach is validated in simulation and hardware experiments on a Franka Panda robotic manipulator. Results demonstrate that the framework can plan motion and/or force trajectories in real time, maximize the manipulation range while avoiding obstacles, and strictly adhere to safety-related hard constraints.

翻译：本文提出一种面向开放工作空间安全高效机器人操作的任务导向模型预测控制（ToMPC）框架。该框架将无碰撞运动与机器人-环境交互相统一，以应对多样化场景。此外，其引入的任务导向避障机制利用运动学冗余性，提升了在障碍环境中的操作效率。该复杂优化问题通过交替方向乘子法（ADMM）求解，该方法将原问题分解为两个子问题，分别采用微分动态规划（DDP）与二次规划（QP）进行处理。通过在Franka Panda机器人操作臂上进行仿真与硬件实验，验证了该方法的有效性。结果表明，该框架能够实时规划运动及/或力轨迹，在规避障碍物的同时最大化操作范围，并严格遵循安全相关的硬约束条件。