From Rocks to Walls: A Model-Free Reinforcement Learning Approach To Dry Stacking With Irregular Rocks
In-situ resource utilization is a key aspect for an efficient human exploration of extraterrestrial environments. A cost-effective method for the construction of preliminary structures is dry stacking with locally found unprocessed rocks. This work focus on learning this task from scratch. Former approaches rely on previously acquired models of rocks, which may be hard to obtain in the context of a mission. In alternative, we propose a model-free, data driven approach. We formulate the problem as the task of selecting the position to place each rock on top of the currently built structure. The rocks are presented to the robot in sequence. The goal is to assemble a wall that approximates a target volume, given the 3D perception of the currently built structure, the next object and the target volume. An agent is developed to learn this task using reinforcement learning. The DQN algorithm is used, where the Q-network outputs a value map corresponding to the expected return of placing the object in each position of a top-view depth image. The learned policy outperforms engineered heuristics, both in terms of stability of the structure and similarity with the target volume. Despite the simplification of the task, the policy learned with this approach could be applied to a realistic setting as the high level planner in an autonomous construction pipeline.