2020. Particle swarm optimization for cooperative multirobot task allocation: a multi-objective approach.

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Multi-step tasks, such as block stacking or parts (dis)assembly, are complex for autonomous robotic manipulation. A robotic system for such tasks would need to hierarchically combine motion control at a lower level and symbolic planning at a higher level. Recently, reinforcement learning (RL) based methods have been shown to handle robotic motion control with better flexibility and generalisability. However, these methods have limited capability to handle such complex tasks involving planning and control with many intermediate steps over a long time horizon.Firstly, current RL systems cannot achieve varied outcomes by planning over intermediate steps (e.g., stacking blocks in different orders). Secondly, exploration efficiency of learning multi-step tasks is low, especially when rewards are sparse.To address these limitations, we develop a unified hierarchical reinforcement learning framework, named Universal Option Framework (UOF), to enable the agent to learn varied outcomes in multi-step tasks. To improve learning efficiency, we train both symbolic planning and kinematic control policies in parallel, aided by two proposed techniques: 1) an auto-adjusting exploration strategy (AAES) at the low level to stabilise the parallel training, and 2) abstract demonstrations at the high level to accelerate convergence.To evaluate its performance, we performed experiments on various multi-step block-stacking tasks with blocks of different shapes and combinations and with different degrees of freedom for robot control. The results demonstrate that our method can accomplish multi-step manipulation tasks more efficiently and stably, and with significantly less memory consumption.

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