Towards self-attention based visual navigation in the real world

Vision guided navigation requires processing complex visual information to inform task-orientated decisions. Applications include autonomous robots, self-driving cars, and assistive vision for humans. A key element is the extraction and selection of relevant features in pixel space upon which to base action choices, for which Machine Learning techniques are well suited. However, Deep Reinforcement Learning agents trained in simulation often exhibit unsatisfactory results when deployed in the real-world due to perceptual differences known as the $\textit{reality gap}$. An approach that is yet to be explored to bridge this gap is self-attention. In this paper we (1) perform a systematic exploration of the hyperparameter space for self-attention based navigation of 3D environments and qualitatively appraise behaviour observed from different hyperparameter sets, including their ability to generalise; (2) present strategies to improve the agents' generalisation abilities and navigation behaviour; and (3) show how models trained in simulation are capable of processing real world images meaningfully in real time. To our knowledge, this is the first demonstration of a self-attention based agent successfully trained in navigating a 3D action space, using less than 4000 parameters.

Adapting a General Purpose Social Robot for Paediatric Rehabilitation through In-situ Design

Socially Assistive Robots (SARs) offer great promise for improving outcomes in paediatric rehabilitation. However, the design of software and interactive capabilities for SARs must be carefully considered in the context of their intended clinical use. While previous work has explored specific roles and functionalities to support paediatric rehabilitation, few have considered the design of such capabilities in the context of ongoing clinical deployment. In this paper we present a two-phase In-situ design process for SARs in health care, emphasising stakeholder engagement and on-site development. We explore this in the context of developing the humanoid social robot NAO as a socially assistive rehabilitation aid for children with cerebral palsy. We present and evaluate our design process, outcomes achieved, and preliminary results from ongoing clinical testing with 9 patients and 5 therapists over 14 sessions. We argue that our in-situ Design methodology has been central to the rapid and successful deployment of our system.