Abstract
This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which showed improved performance without precise controller design. However, DRL-based techniques not only take a long time to learn their policies but also sometimes fail to learn. It is difficult to learn the policy of DRL by random actions only. Therefore, we propose two curricula for the efficient learning of object transportation: region-growing and single- to multi-robot. During the learning process, the region-growing curriculum gradually extended to a region in which an object was initialized. This step-by-step learning raised the success probability of object transportation by restricting the working area. Multiple robots could easily learn a new policy by exploiting the pre-trained policy of a single robot. This single- to multi-robot curriculum can help robots to learn a transporting method with trial and error. Simulation results are presented to verify the proposed techniques.
Highlights
An object transportation technique using robots has been widely applied to diverse fields, such as logistics [1], exploration [2], a retrieval task [3], and service robotics [4]
For concentrating on the effect of the region-growing curriculum, we only consider a single robot ; the case of multiple robots will be addressed
We set the mass of the pallet as 0.1 kg, which is light enough to be manipulated by a single robot
Summary
An object transportation technique using robots has been widely applied to diverse fields, such as logistics [1], exploration [2], a retrieval task [3], and service robotics [4]. Ants can push or pull a large object that is much bigger than their bodies. They know by instinct that working together is better than alone. Inspired by this animal’s cooperative behaviors, many researchers have studied cooperative transportation techniques by imitating their actions. A caging method is an extended pushing method by enclosing an object using multiple robots [8]. They have some advantages, there were many issues, such as requirements for the gripper, precise pushing control, and real-time acquisition of the object shape
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