A precise analysis of an entire image is computationally wasteful if one is interested in finding a target object located in a subregion of the image. A useful "attention strategy" can reduce the overall computation by carrying out fast but approximate image measurements and using their results to suggest a promising subregion. The paper proposes a maximum-likelihood attention mechanism that does this. The attention mechanism recognizes that objects are made of parts and that parts have different features. It works by proposing object part and image feature pairings which have the highest likelihood of coming from the target. The exact calculation of the likelihood as well as approximations are provided. The attention mechanism is adaptive, that is, its behavior adapts to the statistics of the image features. Experimental results suggest that, on average, the attention mechanism evaluates less than 2 percent of all part-feature pairs before selecting the actual object, showing a significant reduction in the complexity of visual search.

We introduce Servomatic, a modular system for robot motion control based on calibration-insensitive visual servoing. A small number of generic motion control operations referred to as primitive skills use stereo visual feedback to enforce a specific task-space kinematic constraint between a robot end-effector and a set of target features. Primitive skills are able to position with an accuracy that is independent of errors in hand-eye calibration and are easily combined to form more complex kinematic constraints as required by different applications. The system has been applied to a number of example problems, showing that modular, high precision, vision-based motion control is easily achieved with off-the-shelf hardware. Our continuing goal is to develop a system where low-level robot control ceases to be a concern to higher-level robotics researchers.

This paper presents initial results in integrating touch with vision for delicate manipulation tasks. A generalizable framework of behavioral primitives for tactile and visual feedback control is proposed. Since vision provides position and shape information at a distance, while tactile provides small-scale geometric and force information, we focus on the complimentary roles of vision and touch. We demonstrate that visual feedback can perform the rough positioning needed for tactile sensor feedback, and that grasp force and object orientation can be sensed and controlled with tactile sensing. A force sensor based approach provides a comparison measure, and we observe that the use of tactile sensing results in a more gentle grasp.