Large offline learning-based models have enabled robots to successfully interact with objects for a wide variety of tasks. However, these models rely on fairly consistent structured environments. For more unstructured environments, an online learning component is necessary to gather and estimate information about objects in the environment in order to successfully interact with them. Unfortunately, online learning methods like Bayesian non-parametric models struggle with changes in the environment, which is often the desired outcome of interaction-based tasks. We propose using an object-centric representation for interactive online learning. This representation is generated by transforming the robot's actions into the object's coordinate frame. We demonstrate how switching to this task-relevant space improves our ability to reason with the training data collected online, enabling scalable online learning of robot-object interactions. We showcase our method by successfully navigating a manipulator arm through an environment with multiple unknown objects without violating interaction-based constraints.

翻译：基于大规模离线学习的模型已使机器人能够成功与物体进行交互以完成各种任务。然而，这些模型依赖于相当一致的结构化环境。对于更非结构化的环境，需要引入在线学习组件来收集和估计环境中物体的信息，以便成功与之交互。不幸的是，贝叶斯非参数模型等在线学习方法难以应对环境变化，而这通常是基于交互任务所期望的结果。我们提出使用面向对象的表示方法进行在线交互学习。这种表示通过将机器人动作转换到物体的坐标系中生成。我们展示了切换到这一任务相关空间如何提升我们利用在线收集训练数据进行推理的能力，从而实现机器人-物体交互的可扩展在线学习。我们通过操控机械臂在包含多个未知物体的环境中成功导航，且不违反基于交互的约束，验证了该方法的效果。