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Visual Grasp Affordance Localization in Point Clouds Using Curved Contact Patches

Detecting affordances on objects is one of the main open problems in robotic manipulation. This paper presents a new method to represent and localize grasp affordances as bounded curved contact patches (paraboloids) of the size of the robotic hand. In particular, given a three-dimensional (3D) point cloud from a range sensor, a set of potential grasps is localized on a detected object by a fast contact patch fitting and validation process. For the object detection, three standard methods from the literature are used and compared. The potential grasps on the object are then refined to a single affordance using their shape (size and curvature) and pose (reachability and minimum torque effort) properties, with respect to the robot and the manipulation task. We apply the proposed method to a circular valve turning task, verifying the ability to accurately and rapidly localize grasp affordances, under significant uncertainty in the environment. We experimentally validate the method with the humanoid robot COMAN on 10 circular control valves fixed on a wall, from five different viewpoints and robot poses for each valve. We compare the reliability of the introduced local grasp affordances method to the baseline that relies only on object detection, illustrating the superiority of ours for the valve turning task.