Human-Machine Interface for Remote Training of Robot Tasks

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and Techniques - IST201

Cobot Programming for Collaborative Industrial Tasks: An Overview

Collaborative robots (cobots) have been increasingly adopted in industries to facilitate human-robot collaboration. Despite this, it is challenging to program cobots for collaborative industrial tasks as the programming has two distinct elements that are difficult to implement: (1) an intuitive element to ensure that the operations of a cobot can be composed or altered dynamically by an operator, and (2) a human-aware element to support cobots in producing flexible and adaptive behaviours dependent on human partners. In this area, some research works have been carried out recently, but there is a lack of a systematic summary on the subject. In this paper, an overview of collaborative industrial scenarios and programming requirements for cobots to implement effective collaboration is given. Then, detailed reviews on cobot programming, which are categorised into communication, optimisation, and learning, are conducted. Additionally, a significant gap between cobot programming implemented in industry and in research is identified, and research that works towards bridging this gap is pinpointed. Finally, the future directions of cobots for industrial collaborative scenarios are outlined, including potential points of extension and improvement

Towards a narrative-oriented framework for designing mathematical learning

This paper proposes a narrative-oriented approach to the design of educational activities, as well as a CSCL system to support them, in the context of learning mathematics. Both Mathematics and interface design seem unrelated to narrative. Mathematical language, as we know it, is devoid of time and person. Computer interfaces are static and non-linear. Yet, as Bruner (1986; 1990) and others show, narrative is a powerful cognitive and epistemological tool. The questions we wish to explore are - - If, and how, can mathematical meaning be expressed in narrative forms - without compromising rigour? - What are the narrative aspects of user interface? How can interface design be guided by notions of narrative? - How can we harness the power of narrative in teaching mathematics, in a CSCL environment? We begin by giving a brief account of the use of narrative in educational theory. We will describe the environment and tools used by the WebLabs project, and report on one of our experiments. We will then describe our narrative-oriented framework, by using it to analyze both the environment and the experiment described

An embedded implementation of Bayesian network robot programming methods

A wide variety of approaches exist for dealing with uncertainty in robotic reasoning, but relatively few consider the propagation of statistical information throughout an entire robotic system. The concept of Bayesian Robot Programming (BRP) involves making decisions based on inference into probability distributions, but can be complex and difficult to implement due to the number of priors and random variables involved. In this work, we apply Bayesian network structures to a modified BRP paradigm to provide intuitive structure and simplify the programming process. The use of discrete random variables in the network can allow high inference speeds, and an efficient programming toolkit suitable for use on embedded platforms has been developed for use on mobile robots. A simple example of navigational reasoning for a small mobile robot is provided as an example of how such a network can be used for probabilistic decisional programming