We address the problem of clearing a pile of unknown objects using an autonomous interactive perception approach. Our robot hypothesizes the boundaries of objects in a pile of unknown objects (object segmentation) and verifies its hypotheses (object detection) using deliberate interactions. To guarantee the safety of the robot and the environment, we use compliant motion primitives for poking and grasping. Every verified segmentation hypothesis can be used to parameterize a compliant controller for manipulation or grasping. The robot alternates between poking actions to verify its segmentation and grasping actions to remove objects from the pile. We demonstrate our method with a robotic manipulator. We evaluate our approach with real-world experiments of clearing cluttered scenes composed of unknown objects.

We present an interactive perceptual skill for segmenting, tracking, and modeling the kinematic structure of 3D articulated objects. This skill is a prerequisite for general manipulation in unstructured environments. Robot-environment interactions are used to move an unknown object, creating a perceptual signal that reveals the kinematic properties of the object. The resulting perceptual information can then inform and facilitate further manipulation. The algorithm is computationally efficient, handles partial occlusions, and depends on little object motion; it only requires sufficient texture for visual feature tracking. We conducted experiments with everyday objects on a robotic manipulation platform equipped with an RGB-D sensor. The results demonstrate the robustness of the proposed method to lighting conditions, object appearance, size, structure, and configuration.

Robust robotic manipulation and perception remains a difficult challenge, in particular in unstructured environments. To address this challenge, we propose to couple manipulation and perception. The robot observes its own deliberate interactions with the world. These interactions reveal sensory information that would otherwise remain hidden and facilitate the interpretation of perceptual data. To demonstrate the effectiveness of interactive perception we present a skill for the manipulation of articulated objects. We show how UMan, our mobile manipulation platform, obtains a kinematic model of an unknown object. The model then enables the robot to perform purposeful manipulation. Our algorithm is extremely robust, and does not require prior knowledge of the object; it is insensitive to lighting, texture, color, specularities, background, and is computationally highly efficient.