Learning Task-Oriented Grasping from Human Activity Datasets

We propose to leverage a real-world, human activity RGB datasets to teach a robot {\em Task-Oriented Grasping} (TOG). On the one hand, RGB-D datasets that contain hands and objects in interaction often lack annotations due to the manual effort in obtaining them. On the other hand, RGB datasets are often annotated with labels that do not provide enough information to infer a 6D robotic grasp pose. However, they contain examples of grasps on a variety of objects for many different tasks. Thereby, they provide a much richer source of supervision than RGB-D datasets. We propose a model that takes as input an RGB image and outputs a hand pose and configuration as well as an object pose and a shape. We follow the insight that jointly estimating hand and object poses increases accuracy compared to estimating these quantities independently of each other. Quantitative experiments show that training an object pose predictor with the hand pose information (and vice versa) is better than training without this information. Given the trained model, we process an RGB dataset to automatically obtain training data for a TOG model. This model takes as input an object point cloud and a task and outputs a suitable region for grasping, given the task. Qualitative experiments show that our model can successfully process a real-world dataset. Experiments with a robot demonstrate that this data allows a robot to learn task-oriented grasping on novel objects.