Martin Peniak

B Sengupta, E Vazquez, M Sasdelli et al.

With the advent of specialized hardware such as Graphics Processing Units (GPUs), large scale image localization, classification and retrieval have seen increased prevalence. Designing scalable software architecture that co-evolves with such specialized hardware is a challenge in the commercial setting. In this paper, we describe one such architecture (\textit{Cortexica}) that leverages scalability of GPUs and sandboxing offered by docker containers. This allows for the flexibility of mixing different computer architectures as well as computational algorithms with the security of a trusted environment. We illustrate the utility of this framework in a commercial setting i.e., searching for multiple products in an image by combining image localisation and retrieval.

Junpei Zhong, Martin Peniak, Jun Tani et al.

The paper presents a neurorobotics cognitive model to explain the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. This cognitive model is based on Multiple Time-scale Recurrent Neural Networks (MTRNN).With the data obtained from object manipulation tasks with a humanoid robot platform, the robotic agent implemented with this model can ground the primitive embodied structure of verbs through training with verb-noun combination samples. Moreover, we show that a functional hierarchical architecture, based on MTRNN, is able to generalise and produce novel combinations of noun-verb sentences. Further analyses of the learned network dynamics and representations also demonstrate how the generalisation is possible via the exploitation of this functional hierarchical recurrent network.