Vision-Based Reacquisition for Task-Level Control

We describe a vision-based algorithm that enables a robot to “reacquire” objects previously indicated by a human user through simple image-based stylus gestures. By automatically generating a multiple-view appearance model for each object, the method can reacquire the object and reconstitute the user’s segmentation hints even after the robot has moved long distances or significant time has elapsed since the gesture. We demonstrate that this capability enables novel command and control mechanisms: after a human gives the robot a “guided tour” of named objects and their locations in the environment, he can dispatch the robot to fetch any particular object simply by stating its name. We implement the object reacquisition algorithm on an outdoor mobile manipulation platform and evaluate its performance under challenging conditions that include lighting and viewpoint variation, clutter, and object relocation.