Solving real-world manipulation tasks requires robots to have a repertoire of skills applicable to a wide range of circumstances. When using learning-based methods to acquire such skills, the key challenge is to obtain training data that covers diverse and feasible variations of the task, which often requires non-trivial manual labor and domain knowledge. In this work, we introduce Active Task Randomization (ATR), an approach that learns robust skills through the unsupervised generation of training tasks. ATR selects suitable tasks, which consist of an initial environment state and manipulation goal, for learning robust skills by balancing the diversity and feasibility of the tasks. We propose to predict task diversity and feasibility by jointly learning a compact task representation. The selected tasks are then procedurally generated in simulation using graph-based parameterization. The active selection of these training tasks enables skill policies trained with our framework to robustly handle a diverse range of objects and arrangements at test time. We demonstrate that the learned skills can be composed by a task planner to solve unseen sequential manipulation problems based on visual inputs. Compared to baseline methods, ATR can achieve superior success rates in single-step and sequential manipulation tasks.

翻译：解决现实世界中的操作任务要求机器人具备一套适用于广泛场景的技能。当使用基于学习的方法来获取此类技能时，关键挑战在于获取涵盖任务多样化且可行变体的训练数据，这通常需要大量的人工劳动和领域知识。在本工作中，我们引入了主动任务随机化（ATR），一种通过无监督生成训练任务来学习鲁棒技能的方法。ATR通过平衡任务的多样性和可行性，选择适合学习鲁棒技能的初始环境状态和操作目标组成的任务。我们提出通过联合学习紧凑的任务表示来预测任务的多样性和可行性。所选任务随后基于图的参数化在仿真中程序化生成。这些训练任务的主动选择使得使用我们的框架训练的技能策略能够在测试时鲁棒地处理多样化的物体和布局。我们证明了所学技能可由任务规划器组合，以解决基于视觉输入的未知顺序操作问题。与基线方法相比，ATR在单步和顺序操作任务中可获得更优的成功率。