When humans perform contact-rich manipulation tasks, customized tools are often necessary to simplify the task. For instance, we use various utensils for handling food, such as knives, forks and spoons. Similarly, robots may benefit from specialized tools that enable them to more easily complete a variety of tasks. We present an end-to-end framework to automatically learn tool morphology for contact-rich manipulation tasks by leveraging differentiable physics simulators. Previous work relied on manually constructed priors requiring detailed specification of a 3D object model, grasp pose and task description to facilitate the search or optimization process. Our approach only requires defining the objective with respect to task performance and enables learning a robust morphology through randomizing variations of the task. We make this optimization tractable by casting it as a continual learning problem. We demonstrate the effectiveness of our method for designing new tools in several scenarios, such as winding ropes, flipping a box and pushing peas onto a scoop in simulation. Additionally, experiments with real robots show that the tool shapes discovered by our method help them succeed in these scenarios.

翻译：人类在执行接触丰富的操作任务时，常需借助定制工具来简化流程。例如，我们使用刀、叉、勺等各式餐具处理食物。同样，机器人也可能受益于专用工具，从而更轻松地完成各类任务。我们提出一个端到端框架，通过利用可微分物理模拟器自动学习接触丰富操作任务的工具形态。以往研究依赖人工构建的先验知识，需详细指定三维物体模型、抓取姿态及任务描述来辅助搜索或优化过程。而我们的方法仅需定义与任务性能相关的目标函数，并通过随机化任务变体实现鲁棒形态学习。我们将该优化问题转化为持续学习问题，使其变得可解。在卷绳、翻转箱子及将豌豆推入铲子等仿真场景中，我们验证了该方法设计新工具的有效性。此外，真实机器人实验表明，由本方法发现的工具形状能帮助它们在这些场景中取得成功。