Thanks to the latest advances in learning and robotics, domestic robots are beginning to enter homes, aiming to execute household chores autonomously. However, robots still struggle to perform autonomous manipulation tasks in open-ended environments. In this context, this paper presents a method that enables a robot to manipulate a wide spectrum of articulated objects. In this paper, we automatically generate different robot low-level trajectory primitives to manipulate given object articulations. A very important point when it comes to generating expert trajectories is to consider the diversity of solutions to achieve the same goal. Indeed, knowing diverse low-level primitives to accomplish the same task enables the robot to choose the optimal solution in its real-world environment, with live constraints and unexpected changes. To do so, we propose a method based on Quality-Diversity algorithms that leverages sparse reward exploration in order to generate a set of diverse and high-performing trajectory primitives for a given manipulation task. We validated our method, QDTraj, by generating diverse trajectories in simulation and deploying them in the real world. QDTraj generates at least 5 times more diverse trajectories for both hinge and slider activation tasks, outperforming the other methods we compared against. We assessed the generalization of our method over 30 articulations of the PartNetMobility articulated object dataset, with an average of 704 different trajectories by task. Code is publicly available at: https://kappel.web.isir.upmc.fr/trajectory_primitive_website

翻译：得益于学习与机器人技术的最新进展，家用机器人开始进入家庭，旨在自主执行家务。然而，机器人在开放环境中执行自主操作任务仍面临挑战。为此，本文提出一种方法，使机器人能够操控多种关节类物体。本文自动生成不同的机器人底层轨迹基元，以操作给定物体的关节结构。生成专家轨迹时的一个关键点在于，需考虑实现同一目标所采用解决方案的多样性。实际上，了解完成同一任务的不同底层基元，能使机器人根据实时约束和意外变化，在实际环境中选择最优方案。为此，我们提出一种基于质量多样性算法的方法，利用稀疏奖励探索来为给定操作任务生成一组多样且高性能的轨迹基元。我们通过仿真环境生成多样轨迹并在真实世界部署，验证了所提方法QDTraj。对于铰链和滑块激活任务，QDTraj生成的轨迹多样性至少提升5倍，优于其他对比方法。我们评估了该方法在PartNetMobility关节物体数据集中30个关节结构上的泛化能力，每个任务平均生成704条不同轨迹。代码已开源：https://kappel.web.isir.upmc.fr/trajectory_primitive_website