TY  - BOOK
AB  - Many motor skills have an intrinsic, low-dimensional parameterization, 
e.g. reaching through a grid to different targets. Repeated policy search 
for new parameterizations of such a skill is inefficient, because the structure 
of the skill variability is not exploited. 
This issue has been previously addressed by learning mappings from task 
parameters to policy parameters. In this work, we introduce a bootstrapping 
technique that establishes such parameterized skills incrementally.
The approach combines iterative learning with state-of-the-art
black-box policy optimization. We investigate the benefits of
incrementally learning parameterized skills for efficient policy
retrieval and show that the number of required rollouts can be
significantly reduced when optimizing policies for novel tasks.
The approach is demonstrated for several parameterized motor
tasks including upper-body reaching motion generation for the
humanoid robot COMAN.
DA  - 2016
DO  - 10.1109/HUMANOIDS.2016.7803281
KW  - memory
KW  - parameterization
KW  - optimization
KW  - learning
LA  - eng
PY  - 2016
TI  - Incremental Bootstrapping of Parameterized Motor Skills
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29068977
Y2  - 2024-11-22T03:34:08
ER  -