Speaker(s) : Andrea Soltoggio

Event : 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO 2012)

Place : Guangzhou, China

Date and Time : 12/12/2012, 3:20 pm

Abstract : Learning and reproducing complex movements is an important skill
for robots. However, while humans can learn and generalise new complex
trajectories, robots are often programmed to execute point-by-point precise
but fixed patterns. This study proposes a method for decomposing new complex
trajectories into a set of known robot-based primitives. Instead of
reproducing accurately an observed trajectory, the robot interprets it as a
composition of its own previously acquired primitive movements. The method
attempts initially a rough approximation with the idea of capturing the most
essential features of the movement. Observing the discrepancy between the
demonstrated and reproduced trajectories, the process then proceeds with
incremental decompositions. The method is tested on both geometric and human
generated trajectories. The shift from a data-centred view to an
agent-centred view in learning trajectories results in generalisation
properties like the abstraction to primitives and noise suppression. This
study suggests a novel approach to learning complex robot motor patterns that
builds upon existing motor skills. Applications include drawing, writing,
movement generation and object manipulation in a variety of tasks.

Partners : Bielefeld University - CoR-Lab