To perform successful manipulation, robots depend on information about objects in their environment. In unstructured environments, such information cannot be given to the robot a priori. It is thus critical for the robot to be able to continuously acquire task-specific information about objects. Towards this goal, we present a robust perceptual skill for identifying, tracking, and segmenting objects in a cluttered environment. We increase the robot's perceptual capabilities by closely coupling them with the robot's manipulation skills. The robot's interaction with objects in the environment creates a perceptual signal, i.e. motion, that renders segmentation and tracking robust and reliable. In addition, the resulting perceptual signal reveals the type of segmentation most relevant to manipulation, namely a segmentation of rigidly connected physical bodies. We demonstrate our approach with experiments on a real world mobile manipulation platform with multiple objects in a cluttered scene.

Flash LADAR cameras based on continuous-wave, time-of-flight range measurement deliver fast 3D imaging for robot applications including mapping, localization, obstacle detection and object recognition. The accuracy of the range values produced depends on characteristics of the scene as well as dynamically adjustable operating parameters of the cameras. In order to optimally set these parameters during camera operation we have devised and implemented an optimization algorithm in a modular, extensible architecture for real-time applications including robot control. The approach uses two components: offline nonlinear optimization to minimize the range error for a training set of simple scenes followed by an online, real-time algorithm to reference the training data and set camera parameters. We quantify the effectiveness of our approach and highlight topics of interest for future research.