PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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).

Abstract

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 simulation.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Haptics
ID Code:5904
Deposited By:Stefan Klanke
Deposited On:08 March 2010