Nonprehensile Dynamic Manipulation: A Survey

Nonprehensile dynamic manipulation can be reason- ably considered as the most complex manipulation task. It might be argued that such a task is still rather far from being fully solved and applied in robotics. This survey tries to collect the results reached so far by the research community about planning and control in the nonprehensile dynamic manipulation domain. A discussion about current open issues is addressed as well

Motion planning and control methods for nonprehensile manipulation and multi-contact locomotion tasks

Many existing works in the robotic literature deal with the problem of nonprehensile dynamic manipulation. However, a unified control framework does not exist so far. One of the ambitious goals of this Thesis is to contribute to identify planning and control frameworks solving classes of nonprehensile dynamic manipulation tasks, dealing with the non linearity of their dynamic models and, consequently, with the inherited design complexity. Besides, while passing through a number of connections between dynamic nonprehensile manipulation and legged locomotion, the Thesis presents novel methods for generating walking motions in multi-contact situations

Memory-Based Neural Networks For Robot Learning

This paper explores a memory-based approach to robot learning, using memorybased neural networks to learn models of the task to be performed. Steinbuch and Taylor presented neural network designs to explicitly store training data and do nearest neighbor lookup in the early 1960s. In this paper their nearest neighbor network is augmented with a local model network, which fits a local model to a set of nearest neighbors. This network design is equivalent to a statistical approach known as locally weighted regression, in which a local model is formed to answer each query, using a weighted regression in which nearby points (similar experiences) are weighted more than distant points (less relevant experiences). We illustrate this approach by describing how it has been used to enable a robot to learn a difficult juggling task. Keywords: memory-based, robot learning, locally weighted regression, nearest neighbor, local models. 1 Introduction An important problem in motor learning is approxim..