Events Calendar

PhD Defense - Franziska Meier

Title: Towards a Probabilistic Motor Skill Learning Framework

Location: RTH 406

Time: 10am - noon, May 3rd, 2016

PhD Candidate: Franziska Meier

Committee members:

Prof. Stefan Schaal (Chair)
Prof. Gaurav Sukhatme
Prof. Yan Liu
Prof. James Finley

Abstract:

While learning in robotics has always been seen as one of the
hallmarks to accomplish autonomous behaviors, so far, there is no coherent and robust approach to robot learning. For instance, realizing complex behaviors, such as manipulation skills, often involves a mixing and matching of planning, control, and learning modules, dominated by the insights of the robotics researcher, but not by a coherent design and/or algorithmic principle. Thus, most robot learning approaches have largely remained a proof-of-concept rather then a general research approach towards robot learning.

In this thesis we aim to move towards a motor skill learning framework coherently routed in probabilistic representations. The use of probabilistc graphical models for different learning modules can foster a principled combination of these modules to form an integrated approach to skill representations. Towards this goal I will present
contributions from two directions: Creating computationally efficient approximations of probabilistic graphical models and developing probabilistic solutions to problems in motor skill learning.

In the first part of my talk I will present our work on scaling up learning in graphical models such that the use of a complex graphical model -- as would be required for complex motor skill representations with perceptual coupling -- is feasible.

In the second part of the talk, I will then present probabilistic approaches to two subproblems of motor skill learning. First I will introduce a probabilistic version of dynamic movement primitives. With the help of this formulation we can implement online movement recognition and perform segmentation of complex skill sequences into movement primitives.

Finally, I will tackle the problem of learning internal models, exemplified by inverse dynamics learning. Having a good inverse dynamics model ensures that we can execute trajectories in an accurate yet compliant manner. I will present a real-time capable drifting Gaussian process approach to learning a local approximation of the
inverse dynamics model on the fly.

Location: Ronald Tutor Hall of Engineering (RTH) - 406

Audiences: Everyone Is Invited

Contact: Lizsl De Leon