Robot Navigation in Unseen Spaces using an Abstract Map

Human navigation in built environments depends on symbolic spatial information which has unrealised potential to enhance robot navigation capabilities. Information sources such as labels, signs, maps, planners, spoken directions, and navigational gestures communicate a wealth of spatial information to the navigators of built environments; a wealth of information that robots typically ignore. We present a robot navigation system that uses the same symbolic spatial information employed by humans to purposefully navigate in unseen built environments with a level of performance comparable to humans. The navigation system uses a novel data structure called the abstract map to imagine malleable spatial models for unseen spaces from spatial symbols. Sensorimotor perceptions from a robot are then employed to provide purposeful navigation to symbolic goal locations in the unseen environment. We show how a dynamic system can be used to create malleable spatial models for the abstract map, and provide an open source implementation to encourage future work in the area of symbolic navigation. Symbolic navigation performance of humans and a robot is evaluated in a real-world built environment. The paper concludes with a qualitative analysis of human navigation strategies, providing further insights into how the symbolic navigation capabilities of robots in unseen built environments can be improved in the future.Comment: 15 pages, published in IEEE Transactions on Cognitive and Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see https://btalb.github.io/abstract_map/ for access to softwar

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

What Can Machines Learn, and What Does It Mean for Occupations and the Economy?

Advances in machine learning (ML) are poised to transform numerous occupations and industries. This raises the question of which tasks will be most affected by ML. We apply the rubric evaluating task potential for ML in Brynjolfsson and Mitchell (2017) to build measures of "Suitability for Machine Learning" (SML) and apply it to 18,156 tasks in O*NET. We find that (i) ML affects different occupations than earlier automation waves; (ii) most occupations include at least some SML tasks; (iii) few occupations are fully automatable using ML; and (iv) realizing the potential of ML usually requires redesign of job task content

Intent prediction of vulnerable road users for trusted autonomous vehicles

This study investigated how future autonomous vehicles could be further trusted by vulnerable road users (such as pedestrians and cyclists) that they would be interacting with in urban traffic environments. It focused on understanding the behaviours of such road users on a deeper level by predicting their future intentions based solely on vehicle-based sensors and AI techniques. The findings showed that personal/body language attributes of vulnerable road users besides their past motion trajectories and physics attributes in the environment led to more accurate predictions about their intended actions

Centaur VGI: Evaluating a human-machine workflow for increased productivity during humanitarian mapping campaigns

The spatial and temporal distribution of global map data is highly unequal, with large areas of the world suffering from a paucity of data. Volunteered geographic information (VGI) has been vaunted as a potential solution, but is also criticised for reinforcing rather than alleviating inequalities. Human- machine workflows have been suggested to improve the speed and quality of VGI production for poorly mapped regions, but this ability is yet to be fully evaluated. This paper provides the first detailed evaluation of a human-machine workflow, testing its ability to produce high quality, timely data in remote regions often neglected by humanitarian mapping campaigns