Interactive Perception : From Scenes to Objects

Sammanfattning: This thesis builds on the observation that robots, like humans, do not have enough experience to handle all situations from the start. Therefore they need tools to cope with new situations, unknown scenes and unknown objects. In particular, this thesis addresses objects. How can a robot realize what objects are if it looks at a scene and has no knowledge about objects? How can it recover from situations where its hypotheses about what it sees are wrong? Even if it has built up experience in form of learned objects, there will be situations where it will be uncertain or mistaken, and will therefore still need the ability to correct errors. Much of our daily lives involves interactions with objects, and the same will be true robots existing among us. Apart from being able to identify individual objects, the robot will therefore need to manipulate them.Throughout the thesis, different aspects of how to deal with these questions is addressed. The focus is on the problem of a robot automatically partitioning a scene into its constituting objects. It is assumed that the robot does not know about specific objects, and is therefore considered inexperienced. Instead a method is proposed that generates object hypotheses given visual input, and then enables the robot to recover from erroneous hypotheses. This is done by the robot drawing from a human's experience, as well as by enabling it to interact with the scene itself and monitoring if the observed changes are in line with its current beliefs about the scene's structure.Furthermore, the task of object manipulation for unknown objects is explored. This is also used as a motivation why the scene partitioning problem is essential to solve. Finally aspects of monitoring the outcome of a manipulation is investigated by observing the evolution of flexible objects in both static and dynamic scenes. All methods that were developed for this thesis have been tested and evaluated on real robotic platforms. These evaluations show the importance of having a system capable of recovering from errors and that the robot can take advantage of human experience using just simple commands.