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— 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(More)
— We introduce a learning-based approach to manipulation in unstructured environments. This approach permits autonomous acquisition of manipulation expertise from interactions with the environment. The resulting expertise enables a robot to perform effective manipulation based on partial state information. The manipulation expertise is represented in a(More)
— Research in Autonomous Mobile Manipulation critically depends on the availability of adequate experimental platforms. In this paper, we describe an ongoing effort at the University of Massachusetts Amherst to construct a hardware platform with redundant kinematic degrees of freedom, a comprehensive sensor suite, and significant end-effector capabilities(More)
—Autonomous manipulation in unstructured environments presents roboticists with three fundamental challenges: object segmentation, action selection, and motion generation. These challenges become more pronounced when unknown man-made or natural objects are cluttered together in a pile. We present an end-to-end approach to the problem of manipulating unknown(More)
— 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(More)