QDTraj：探索多样化轨迹原语用于机械臂物体操控
QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation
AI总结：
本文提出QDTraj方法，通过生成多样化的低级轨迹原语，提升机器人在开放环境中操控多样化机械臂物体的能力，实验表明其在模拟和现实部署中均表现优异。
摘要：
得益于学习和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，机器人仍难以在开放环境中自主完成操控任务。本文提出一种方法，使机器人能够操控广泛种类的机械臂物体。本文自动生成不同机器人低级轨迹原语以操控给定物体的关节结构。生成专家轨迹时，考虑解决方案的多样性以实现相同目标至关重要。了解多样化的低级原语能帮助机器人在现实环境中选择最优解，面对实时约束和意外变化。为此，我们提出基于质量-多样性算法的方法，利用稀疏奖励探索生成一组多样化且高性能的轨迹原语。我们通过模拟生成多样化轨迹并部署到现实世界中验证了该方法。QDTraj在铰链和滑动激活任务中生成的轨迹多样性至少比其他方法多5倍。我们对PartNetMobility数据集中的30种关节物体进行了泛化测试，平均每个任务生成704种不同轨迹。代码已公开：https://kappel.web.isir.upmc.fr/trajectory_primitive_website
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
