Learning from Demonstration (LfD) provides an intuitive and fast approach to program
robotic manipulators. Task parameterized representations allow easy adaptation to new …

Learning from Demonstration (LfD) stands as an efficient framework for imparting human-
like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly …

Overhead work involving the construction and maintenance of civil infrastructure (eg,
tunnels, overpasses, and buildings) is strenuous and fatigue-inducing for human workers …

Enabling robots to quickly learn manipulation skills is an important, yet challenging problem.
Such manipulation skills should be flexible, eg, be able adapt to the current workspace …

Robotic manipulation is currently undergoing a profound paradigm shift due to the
increasing needs for flexible manufacturing systems, and at the same time, because of the …

This paper explores the problem of automated robot program generation from limited
historical data when neither accurate geometric environmental models nor online vision …

Complex manipulation tasks can contain various execution branches of primitive skills in
sequence or in parallel under different scenarios. Manual specifications of such branching …

Abstract Task Parametrised Gaussian Mixture Modelling and Regression (TP-GMM/R) is an
eminent algorithm to enable collaborative robots (cobots) to adapt to new environments …

Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to
program robot skills, which has gained growing interest both in research and industrial …

Manufacturing plays a key role in the economy, and robotics plays a key role in
manufacturing. Developments in both areas are soaring, driven in part by growing caches of …