Close and precise placement of irregularly shaped objects requires a skilled robotic system. Particularly challenging is the manipulation of objects that have sensitive top surfaces and a fixed set of neighbors. To avoid damaging the surface, they have to be grasped from the side, and during placement, their neighbor relations have to be maintained. In this work, we train a reinforcement learning agent that generates smooth end-effector motions to place objects as close as possible next to each other. During the placement, our agent considers neighbor constraints defined in a given layout of the objects while trying to avoid collisions. Our approach learns to place compact object assemblies without the need for predefined spacing between objects as required by traditional methods. We thoroughly evaluated our approach using a two-finger gripper mounted to a robotic arm with six degrees of freedom. The results show that our agent outperforms two baseline approaches in terms of object assembly compactness, thereby reducing the needed space to place the objects according to the given neighbor constraints. On average, our approach reduces the distances between all placed objects by at least 60%, with fewer collisions at the same compactness compared to both baselines.

翻译：不规则形状物体的紧密精准放置需要高技能机器人系统，尤其当物体具有敏感顶面且需保持固定邻接关系时更具挑战性。为避免损坏顶面，必须从侧面抓取物体，并在放置过程中维持它们的邻接关系。本研究训练了一个强化学习智能体，该智能体能生成平滑的末端执行器运动轨迹，以实现物体间最大程度的紧邻放置。在放置过程中，智能体需满足给定物体布局中定义的邻接约束条件，同时避免碰撞。与传统方法需要预设物体间距不同，我们的方法无需预先定义间距即可学会放置紧凑物体组合。我们采用安装在六自由度机械臂上的双指夹爪进行了全面评估。结果表明，在物体组合紧凑性方面，本智能体表现优于两种基线方法，从而在满足指定邻接约束条件下减少了物体放置所需空间。平均而言，本方法将所有放置物体间距缩减至少60%，在保持相同紧凑度的同时，碰撞次数少于两种基线方法。