4.1 Representation levels in visual space specification



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4.1 Representation levels in visual space specification

The representations used in a perceptual action visual specification and control system using visual goals are very different from those in a traditional control system centered around the world coordinate reference frame. The central representations in a perceptual action, system as shown in fig. 12 are perception or feature vectors y. From one perspective, that of the feedback controller, these are just vectors of real numbers. From a task perspective, however, the vectors need to capture the parts of the system state we are interested in manipulating, and be well conditioned with respect to the task we are performing. In other words the perceptual goals have to be good attractors.

One way of looking at the situation is to view robot control as an optimization problem of transforming the initial perceptual state, denoted to the goal state . In the perceptual action paradigm, the perception vector space is both the space of specification, and the space in which the controller operates. Visual servoing, is an attempt to solve the optimization problem by gradient descent in this space. Closing of the feedback loop allows servoing to zero perceptual visual error without accurate calibration needed in a traditional system servoing only in robot joint space. The convergence of the algorithm clearly depends on the qualities of the measurements.

  
Figure 12: The representation levels in a Perceptual Action system. The main level is the Perception vectors. Goals are specified as perception vectors, and when moving they represent all state information relevant for attaining the goal

The perception vectors contains all the necessary information to describe the task. The adaptive visual feedback controller we presented in the previous sections, without initial knowledge about , provides an effective method of finding a control signal sequence , that efficiently takes the system through the states , while at the same time learning about .

In the important case of a perception vector based only on vision, each perception vector corresponds to a measure in some image . The idea is that is chosen to be a fingerprint of . The fingerprint should be unique, but only in a very weak sense. It should assign the value only to images of world configurations which fulfill our goal, and values close to only to images which are close to the goal. Thus if we have a sequence of goals we can think of the sequence as a sequence of goal images, although in reality they are measurements of the image. This is the key to efficient teaching of the system, since producing a set of images for a task, and letting the vision system automatically extract the measurements is considerably easier than the traditional trajectory specification method used in conventional robot control.

A large number of different visual measures have been proposed for visual feedback, including line length, projected area, area or line length ratios [Weiss, Sand 87], and Fourier descriptors [Feddema Mitchell 89]. Mitch Harris in [Harris 93] defines a more general ``visual similarity'' or ``closeness'' function based on orientation, length, and distance between line segments. Our philosophy has been to use the simplest measures that suffice for the task at hand.

We tried various measures for our perception vectors. As it turned out, very little structural sophistication was needed to obtain perceptual measures that permitted very good performance for a range of positional tasks. The image locations of tracked object features in different cameras (without correspondence) provided satisfactory perceptual features for all the experiments in this paper. The dimension of our image space then depends on the number of features tracked, one feature in one camera giving two scalar values in a -vector. The task specifications took one of two forms. Either they specified the desired image locations of various tracked feature points in various cameras, or they specified pairs of tracked feature points that were to be brought into some sort of correspondence with each other (e.g. bringing a rod end to a hole). This latter specification can be used in applications involving a moving camera or an autonomously moving object.

At a higher level, a perceptual action system can utilize a sequence of goal perceptions (feature vectors if we are using vision), each being a subgoal in a task, thus implementing a cycle precondition perception action perception, where perception is a goal perception. This can be seen as an extension of the precondition perception action used in many subsumption architecture systems [Brooks 91][Brooks 86][Connell 91], where a process is watching sensory inputs for a precondition perception, and when a matching perception occurs, an open loop, canned action is carried out. Our approach extends on the subsumption idea so that the action is no longer open loop, but under continuous monitoring in perceptual space attempting to reach a specific perceptual goal.



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