TY  - BOOK
AB  - Striving for an autonomous self-exploration of
robots to learn their own body schema, i.e. body shape and
appearance, kinematic and dynamic parameters, association
of tactile stimuli to specific body locations, etc., we developed
a tactile-servoing feedback controller that allows a robot to
continuously acquire self-touch information while sliding a
fingertip across its own body. In this manner one can quickly
acquire a large amount of training data representing the body
shape.
We compare three approaches to track the common contact
point observed when one robot arm is touching the other in a
bimanual setup: feedforward control, solely relying on a coarse
CAD-based kinematics performs worst, a solely feedback-based
controller typically lacks behind, and only the combination of
both approaches yields satisfactory tracking results.
As a first, preliminary application, we use this self-touch
capability to calibrate the closed kinematic chain formed by
both arms touching each other. The obtained homogeneous
transform describing the relative mounting pose of both arms
improves end-effector position estimations by a magnitude.
DA  - 2015
LA  - eng
PY  - 2015
TI  - Towards Body Schema Learning using Training Data Acquired by Continuous Self-touch
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-27315414
Y2  - 2024-11-22T13:25:40
ER  -