M.Sc. Thesis: Svenja Stark on Learning Probabilistic Feedforward and Feedback Policies for Stable Walking

Supervisors: Elmar Rueckert, Prof. Dr. Jan Peters

Finished: 13.Januar.2016

Abstract

The compliant quadruped robot Oncilla is used as platform to explore the benefits of the interaction between a feedforward gait and a simultaneous active stabilizing feedback controller. The chosen approach is motivated by findings in biology as well as by the advantages of modern stochastic methods. In this thesis, we present a balancing controller, a simple feedforward gait and first results of a system combining both components. The basic components can be modified in further research.

The developed balancing controller is based on a common criteria for static stability, the current center of pressure (CoP) of the Oncilla. It is calculated from force data obtained from mounted OptoForce sensors and the endeffector positions calculated by a simplified forward kinematic model. Locally weighted regression is used to calculate motor commands that bring the Oncilla’s CoP closer to a desired one.

The static walking gait is based on analyses of the walking behavior of four-legged animals. The trajectory for each leg is further carefully hand-tuned and parameterized as a combination of sine curves. In addition, rhythmic movement primitives modulating the handcrafted gait have been applied.

Finally, we explored how the final gait is influenced by combining the forward gait and the balancing controller. The tuning parameter was the amount of applied feedback, which determines how much the Oncilla relies on the gait or on the feedback. In the current setup, the balancing controller could not visibly improve the feedforward gait.

This thesis provides first results towards a versatile platform allowing further experiments on the benefits of feedback for gait learning.

Thesis

Learning Probabilistic Feedforward and Feedback Policies for Stable Walking