Patricia Shaw

Single-track roads are narrow bi-directional roads often found in UK rural areas. They are vital to local communities but pose various challenges to drivers and the simulation of traffic. One key aspect of navigating single-track roads is managing dedicated passing places, short widenings of the road that allow drivers to pass each other. State-of-the-art traffic simulators lack the full functionality to model and resolve passing place scenarios on single-track roads, thus limiting their effectiveness in simulating these environments. This paper contributes to this gap through determining whether the guidance provided in the UK Highway Code is capable of resolving the passing place problem. Using AgentSpeak and a BDI framework, autonomous agents were programmed with the behaviours governed by the Highway Code and evaluated in a number of simulated scenarios. Results show that the Highway Code can improve performance over the state-of-the-art, but struggles to resolve more complex real-world passing place scenarios.

As chairs of the UKRAS 2022 conference, we are happy to welcome you in person after a break from in-person events. The theme of this year’s conference is “Robotics for Unconstrained Environments”, reflecting much of the robotics research that happens at Aberystwyth University. Unconstrained environments include any indoor and outdoor environment that has not been modified specifically for the robot to perform its task. The premise is that the environment must be representative of the task rather than being artificially simplified.

(Abstracted from Gynecol Oncol 2021;162:97–106) High-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian cancer cases and is the deadliest subtype of ovarian cancer. Approximately 20% of ovarian cancers are hereditary, and the greatest single risk factor for ovarian cancer is the inheritance of a pathogenic variant (PV) such as BRCA1 or BRCA2.

Exercising sensorimotor and cognitive functions allows humans, including infants, to interact with the environment and objects within it. In particular, during everyday activities, infants continuously enrich their repertoire of actions, and by playing, they experimentally plan such actions in sequences to achieve desired goals. The latter, reflected as perceptual target states, are built on previously acquired experiences shaped by infants to predict their actions. Imitating this, in developmental robotics, we seek methods that allow autonomous embodied agents with no prior knowledge to acquire information about the environment. Like infants, robots that actively explore the surroundings and manipulate proximate objects are capable of learning. Their understanding of the environment develops through the discovery of actions and their association with the resulting perceptions in the world. We extend the development of Dev-PSchema, a schema-based, open-ended learning system and examine the infant-like discovery process of new generalized skills while engaging with objects in free-play using an iCub robot. Our experiments demonstrate the capability of Dev-PSchema to utilize the newly discovered skills to solve user-defined goals beyond its past experiences. The robot can generate and evaluate sequences of interdependent high-level actions to form potential solutions and ultimately solve complex problems toward tool-use.

Editorial: Modeling Play in Early Infant Development