Planning Large-scale Object Rearrangement Using Deep Reinforcement Learning

Object rearrangement is about moving a set of objects from an initial state to a goal state through task and motion planning. Existing methods either show poor scalability in number of objects they can handle, or do not generalize well across situations, or need explicit running buffers to avoid collisions during placements. In this paper, we propose a deep-RL based task planning method to solve large-scale object rearrangement problems. Given the source and target state of objects in the form of images, our method determines a collision-free object movement plan. Our method produces a feasible plan in discrete-continuous action space where picking the selected objects are discrete actions followed by a set of continuous actions to place the object. We propose a novel hierarchical dense reward structure to train our deep-RL network to make our method more sample efficient using the AI2Thor simulator. We show that our method works well on unseen publicly available datasets and on a publicly available simulation environment such as Pybullet thereby demonstrating the superiority of our method in terms of generalizability. To the best of our knowledge, our method is the first one that demonstrates the rearrangement across different scenarios from 2D surfaces such as tabletops to 3D rooms with a large number of objects and without any explicit need of buffer space.