The skill of pivoting an object with a robotic system is challenging for the external forces that act on the system, mainly given by contact interaction. The complexity increases when the same skills are required to generalize across different objects. This paper proposes a framework for learning robust and generalizable pivoting skills, which consists of three steps. First, we learn a pivoting policy on an ``unitary'' object using Reinforcement Learning (RL). Then, we obtain the object's feature space by supervised learning to encode the kinematic properties of arbitrary objects. Finally, to adapt the unitary policy to multiple objects, we learn data-driven projections based on the object features to adjust the state and action space of the new pivoting task. The proposed approach is entirely trained in simulation. It requires only one depth image of the object and can zero-shot transfer to real-world objects. We demonstrate robustness to sim-to-real transfer and generalization to multiple objects.

翻译：使用机器人系统对物体进行枢轴操作的技能具有挑战性，这主要是由于接触交互作用于系统的外力所致。当同一技能需要泛化到不同物体时，复杂性进一步增加。本文提出一个学习鲁棒且可泛化枢轴技能的框架，包含三个步骤。首先，通过强化学习在"单一"物体上学习枢轴操作策略。其次，通过监督学习获得物体的特征空间，以编码任意物体的运动学属性。最后，为使单一策略适应多物体场景，我们基于物体特征学习数据驱动的投影方法，调整新枢轴任务的状态空间与动作空间。该方法完全在仿真环境中训练，仅需单个物体的深度图像即可零样本迁移至真实物体。我们验证了该方法对仿真到现实迁移的鲁棒性以及到多物体的泛化能力。