14 accepted papers
Learning from demonstration can be an effective way to teach robots task-oriented policies. However, in an interactive setting when demonstrations are limited by time or other budgetary constraints, it is challenging to find those that fix the learner's (remaining) errors. This is especially difficu…
Recent evidence has shown that, contrary to expectations, it is difficult for novices to teach robots tasks through learning from demonstration (LfD). Novices often struggle with understanding the relationship between robot states and actions, leading to suboptimal demonstrations. This paper introdu…
Learning from demonstration (LfD) is a technique that allows expert teachers to teach task-oriented skills to robotic systems. However, the most effective way of guiding novice teachers to approach expert-level demonstrations quantitatively for specific teaching tasks remains an open question. To th
Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. With comfortable electronic-textiles, sensors can be embedded into clothing so that it is possible to record human movement outside the laboratory for long periods. However,…
We propose event-based visual microphone (EBVM), a passive electro-optical technique for capturing audio signals remotely using an event camera. The event-based camera records local deformations of a surface induced by the sound propagation by observing the changes in specular reflections at each pi…
Presents corrections to the above named paper.
Wearable motion sensing in daily life has attracted attention in various disciplines. Especially, stretchable strain sensors have been instrumented into garments (e.g. brace). To estimate joint motions from such sensors, previous studies have modelled relationships between the sensor strains and mot
Learning from Demonstration depends on a robot learner generalising its learned model to unseen conditions. Generally, it is not feasible for a person to provide demonstrations that account for all possible variations in non-trivial tasks. While there are many learning methods that can handle interp…
With the increase in complexity of robotic systems and the rise in non-expert users, it can be assumed that task constraints are not explicitly known. In tasks where avoiding singularity is critical to success, this paper provides an approach, especially for non-expert users, for the system to learn…
Increasing research efforts have been made to improve the energy efficiency of variable impedance actuators (VIAs) through reduction of energy consumption. However, the harvesting of dissipated energy in such systems remains under-explored. This study proposes a novel variable damping module design…
The low-cost manufacturing and maintenance of prostheses is of vital importance to their successful deployment in developing countries. Low-cost prosthesis actuation is generally achieved by combining pre-programmed control strategies, with surface-electromyographic measurements taken from the resid…
``Fabric-embedded sensors” are of growing interest in clinical diagnostics and rehabilitation studies that desire the measurement and analysis of human movement outside the laboratory environment. A major issue limiting their usage is the undesired effect of fabric motion artefacts corrupting moveme
Many human skills can be described in terms of performing a set of prioritised tasks. While a number of tools have become available that recover the underlying control policy from constrained movements, few have explicitly considered learning how constraints should be imposed in order to perform the…
Many everyday human skills can be considered in terms of performing some task subject to a set of self-imposed or environmental constraints. In recent years, a number of new tools have become available in the learning and robotics community that allow data from constrained and/or redundant systems t…