TY  - EDBOOK
AB  - We present a neural network approach to learn inverse kinematics
of the humanoid robot ASIMO, where we focus on bi-manual
tool use. The learning copes with both the highly redundant inverse
kinematics of ASIMO and the additional arbitrary constraint imposed
by the tool that couples both hands. We show that this complex kinematics
can be learned from few ground-truth examples using an efficient
recurrent reservoir framework, which has been introduced previously for
kinematics learning and movement generation. We analyze and quantify
the network’s generalization for a given tool by means of reproducing
the constraint in untrained target motions.
DA  - 2010
DO  - 10.1007/978-3-642-15193-4_45
KW  - CoR-Lab Publication
LA  - eng
PY  - 2010
SN  - 978-3-642-15192-7
TI  - Learning Inverse Kinematics for Pose-Constraint Bi-Manual Movements
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-21420253
Y2  - 2024-11-22T01:23:24
ER  -