In an active or behavioral vision system the acquisition of visual
information is not an independent open loop process, but instead
depends on the active agent's interaction with the world. Also the
information need not represent an abstract, high level model of the
world, but instead is highly task specific, and represented in a form
which facilitates particular operations. Visual servoing, when
supplemented with
on-line visual model estimation, fits into the active vision
paradigm.
Results with visual servoing
and varying degrees of model adaption
have been presented for robot arms
[1, 3, 2, 5, 6, 9, 13, 15, 16, 18]
.
Visual models suitable for specifying visual alignments have
also been studied [19, 8, 7].
However, the focus of this work has been the movement (servoing) of the robot,
not on on-line estimation of high DOF visual-motor models.
In this paper we focus on the model exploration aspect and present an
active vision technique, having interacting action (control),
visual sensing and model building modules, which allows the
simultaneous visual-motor model estimation and control (visual
servoing) of a variety of robotic active agents.
We place the following requirements on the active visual model acquisition:
A combined model acquisition and control approach has many advantages. In addition to permitting uncalibrated visual servo control, the on-line estimated models are useful for (1) prediction and constraining search in visual tracking [13, 2], (2) perfomring local coordinate transformations between manipulator (joint), world, and visual frames[9, 13], and (3) synthesizing views from a basis of agent poses [11]. We have found such an adaptive approach to be particularly helpful in robot arm manipulation when carrying out difficult tasks, such as manipulation of flexible material [9, 13], or performing large rotations for exploring object shape [12]. For a dextrous multifinger robot hand, such as the Utah/MIT hand, the fully adaptive approach is appealing because dextrous manipulation of a grasped object is much harder to model accurately than a typical robot arm system, where the object is rigidly attached to the end effector.
In this paper we present four main contributions: