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Effective robot programming by demonstration requires the availability of multiple demonstrations to learn about all relevant aspects of the demonstrated skill or task. Typically, a human teacher must demonstrate several variants of the desired task to generate a sufficient amount of data to reliably learn it. Here a problem often arises that there is a large variability in the speed of execution across human demonstrations. This can cause problems when multiple demonstrations are compared to extract the relevant information for learning. In this paper we propose an extension of dynamic movement primitives called arc-length dynamic movement primitives, where spatial and temporal components of motion are well separated. We show theoretically and experimentally that the proposed representation can be effectively applied for robot skill learning and action recognition even when there are large variations in the speed of demonstrated movements.

In an increasingly competitive manufacturing industry it is becoming ever more important to rapidly react to changes in market demands. In order to satisfy these requirements, it is crucial that automated manufacturing processes are flexible and can be adapted to new production requirements quickly. In this paper we present a novel automatically reconfigurable robot workcell that addresses the issues of flexible manufacturing. The proposed workcell is reconfigurable in terms of hardware and software. The hardware elements of the workcell, both those selected off-the-shelf and those developed specifically for the system, allow for fast cell setup and reconfiguration, while the software aims to provide a modular, robotindependent, ROS-based programming environment. While the proposed workcell is being developed in such a way as to address the needs of production-oriented SMEs where batch sizes are relatively small, it will also be of interest to enterprises with larger production lines since it additionally targets high performance in terms of speed, interoperability of robotic elements, and ease of use.

As robotic systems have become more and more complex and difficult to manage, various software architectures, libraries and programming paradigms have been introduced aimed at high-level control and integration of their constituent parts. The Robot Operating System (ROS) has, for many, become the de facto software framework for communication standardisation and hardware interface abstraction, and various packages within its ecosystem have come to the fore as being reliable design choices for dictating control flow. ROSbased software packages that use state machines as their core methodology to bridge the gap between low-level imperative task scripting and higher-level task planning have proven particularly popular. However, while they provide much in terms of power and flexibility, their overall task-level simplicity can often be obfuscated at the script-level by boilerplate code, intricate structure and lack of code reuse between state machine prototypes. In this paper, we aim to address this deficit by proposing a code generation, templating and metascripting methodology for state machine assembly, as well as an accompanying application programming interface (API), for the rapid, modular development of robot control programs. The API has been developed to function effectively as either a frontend for concise scripting or a back-end for code generation for visual programming systems. Its capabilities are demonstrated in an experiment using a simulated humanoid robot platform.

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