We introduce a framework for cooperative manipulation, applied on an underactuated manipulation problem. Two stationary robotic manipulators are required to cooperate in order to reposition an object within their shared work space. Control of multi-agent systems for manipulation tasks cannot rely on individual control strategies with little to no communication between the agents that serve the common objective through swarming. Instead a coordination strategy is required that queries subtasks to the individual agents. We formulate the problem in a Task And Motion Planning (TAMP) setting, while considering a decomposition strategy that allows us to treat the task and motion planning problems separately. We solve the supervisory planning problem offline using deep Reinforcement Learning techniques resulting into a supervisory policy capable of coordinating the two manipulators into a successful execution of the pick-and-place task. Additionally, a benefit of solving the task planning problem offline is the possibility of real-time (re)planning, demonstrating robustness in the event of subtask execution failure or on-the-fly task changes. The framework achieved zero-shot deployment on the real setup with a success rate that is higher than 90%.

翻译：我们提出了一种针对欠驱动操作问题的协作操控框架。两台固定式机器人操作臂需在其共享工作空间内协作，以实现对物体的重新定位。多智能体系统在操控任务中的控制无法依赖个体控制策略（各智能体之间缺乏或仅存在少量通信，通过群体行为实现共同目标），而需要一种协调策略，将子任务分配给各智能体。我们将该问题形式化为任务与运动规划（TAMP）框架，同时采用一种分解策略，使得任务规划与运动规划问题得以分别处理。通过使用深度强化学习技术，我们对监督规划问题进行了离线求解，从而得到一种监督策略，该策略能够协调两台操作臂成功完成拾取与放置任务。此外，离线求解任务规划问题的优势在于可实现实时（重新）规划，从而在子任务执行失败或任务动态变化时展现出鲁棒性。该框架在实际系统上实现了零样本部署，成功率超过90%。