Towards affordance detection for robot manipulation using affordance for parts and parts for affordance

As robots start to interact with their environments, they need to reason about the affordances of objects in those environments. In most cases, affordances can be inferred only from parts of objects, such as the blade of a knife for cutting or the head of a hammer for pounding. We propose an RGB-D part-based affordance detection method where the parts are obtained based on the affordances as well. We show that affordance detection benefits from a part-based object representation since parts are distinctive and generalizable to novel objects. We compare our method with other state-of-the-art affordance detection methods on a benchmark dataset (Myers et al. in International conference on robotics and automation (ICRA), 2015), outperforming these methods by an average of 14% on novel object instances. Furthermore, we apply our affordance detection method to a robotic grasping scenario to demonstrate that the robot is able to perform grasps after detecting the affordances.