Realising Dextrous Manipulation with Structured Manifolds using Unsupervised Kernel Regression with Structural Hints
Jan Steffen, Stefan Klanke, Sethu Vijayakumar and Helge Ritter
In: ICRA 2009 Workshop: Approaches to Sensorimotor Learning on Humanoid Robots, Kobe, Japan(2009).
Dextrous manipulation based on techniques for
non-linear dimension reduction and manifold learning is an
upcoming field of research and offers promising opportunities.
Still, many problems remain unsolved and researchers are
seeking for new representations that combine efficient learning
of examples and robust generalisation to unseen situations.
Here, we propose a manifold representation of hand postures,
which due to its structural clarity lends itself to simple and
robust manipulation control schemes. Focussing on cyclic movements,
we describe extensions to the dimensionality reduction
algorithm Unsupervised Kernel Regression (UKR) that allow to
incorporate structural hints about the training data into the
learning yielding task-related structures in the manifold’s latent
space. We present the resulting manifold representation and a
simplified controller using this representation for manipulation
in the example of turning a bottle cap in a physics-based