The world outside our laboratories seldom conforms to the assumptions of our models. This is especially true for dynamics models used in control and motion planning for complex high–degree of freedom systems like deformable objects. We must develop better models, but we must also consider that, no matter how powerful our simulators or how big our datasets, our models will sometimes be wrong. What is more, estimating how wrong models are can be difficult, because methods that predict uncertainty distributions based on training data do not account for unseen scenarios. To deploy robots in unstructured environments, we must address two key questions: When should we trust a model and what do we do if the robot is in a state where the model is unreliable. We tackle these questions in the context of planning for manipulating rope-like objects in clutter. Here, we report an approach that learns a model in an unconstrained setting and then learns a classifier to predict where that model is valid, given a limited dataset of rope-constraint interactions. We also propose a way to recover from states where our model prediction is unreliable. Our method statistically significantly outperforms learning a dynamics function and trusting it everywhere. We further demonstrate the practicality of our method on real-world mock-ups of several domestic and automotive tasks.

我们实验室之外的世界很少符合我们模型的假设。对于在复杂的高自由度系统（如可变形物体）的控制和运动规划中使用的动力学模型尤其如此。我们必须开发更好的模型，但我们还必须考虑到，无论我们的模拟器有多强大或我们的数据集有多大，我们的模型有时都会出错。更重要的是，估计模型错误的程度可能很困难，因为基于训练数据预测不确定性分布的方法没有考虑到看不见的场景。为了在非结构化环境中部署机器人，我们必须解决两个关键问题：我们什么时候应该信任模型，如果机器人处于模型不可靠的状态，我们该怎么办。我们在规划处理杂乱绳状物体的背景下解决这些问题。在这里，我们报告了一种方法，该方法在不受约束的设置中学习模型，然后在给定绳约束交互的有限数据集的情况下学习分类器来预测该模型的有效位置。我们还提出了一种从模型预测不可靠的状态中恢复的方法。我们的方法在统计上显着优于学习动态函数并在任何地方信任它。我们进一步证明了我们的方法在几个家庭和汽车任务的真实模型上的实用性。我们的方法在统计上显着优于学习动态函数并在任何地方信任它。我们进一步证明了我们的方法在几个家庭和汽车任务的真实模型上的实用性。我们的方法在统计上显着优于学习动态函数并在任何地方信任它。我们进一步证明了我们的方法在几个家庭和汽车任务的真实模型上的实用性。