This video is accompanied with our humanoids 2015 paper.
"Towards Body Schema Learning using Training Data
Acquired by Continuous Self-touch".
To augment traditionally vision-based body
schema learning with a sensory channel that provides more
accurate positional information, we propose 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: feed-forward control, solely relying on a
CAD-based kinematics, performs worst; a controller that is
only based on tactile feedback typically lacks behind; only the
combination of both approaches yields satisfactory results.
As a first, preliminary application, we use the 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
compared to a traditional, vision-based approach.
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