As a step towards systems that can acquire knowledge automatically, we have designed a system that can learn new objects with a minimum of user interaction and implemented it on our robot platform GripSee (M. Becker et al., 1999). A novel object is placed into the robot's gripper in order to define a default orientation and a default grip. The robot then places the object on a turning table and builds up a visual representation that consists of a collection of graphs, labeled with multiscale edges. A user interface that can correct errors in the representation is also part of the system. The visual representation is complemented by a grip library, which contains possible ways of grasping and manipulating the object in a robust manner. We regard this procedure as an example of human assisted learning.
[1]
Flexible Object Recognition for a Grasping Robot
,
2022
.
[2]
Eric Maël.
Adaptive and flexible robotics for visual and tactile grasping
,
1999
.
[3]
Joachim M. Buhmann,et al.
Distortion Invariant Object Recognition in the Dynamic Link Architecture
,
1993,
IEEE Trans. Computers.
[4]
R. A. Brooks,et al.
Intelligence without Representation
,
1991,
Artif. Intell..
[5]
Jochen Triesch,et al.
GripSee: A Gesture-Controlled Robot for Object Perception and Manipulation
,
1999,
Auton. Robots.
[6]
Christoph von der Malsburg,et al.
Self Calibration of the Fixation Movement of a Stereo Camera Head
,
1998,
Auton. Robots.