On embodied memetic evolution and the emergence of behavioural traditions in Robots

This paper describes ideas and initial experiments in embodied imitation using e-puck robots, developed as part of a project whose aim is to demonstrate the emergence of artificial culture in collective robot systems. Imitated behaviours (memes) will undergo variation because of the noise and heterogeneities of the robots and their sensors. Robots can select which memes to enact, and-because we have a multi-robot collective-memes are able to undergo multiple cycles of imitation, with inherited characteristics. We thus have the three evolutionary operators: variation, selection and inheritance, and-as we describe in this paper-experimental trials show that we are able to demonstrate embodied movement-meme evolution. © 2011 Springer-Verlag

THERAPIST: Towards an Autonomous Socially Interactive Robot for Motor and Neurorehabilitation Therapies for Children

Neurorehabilitation therapies exploiting the use-dependent plasticity of our neuromuscular system are devised to help patients who suffer from injuries or diseases of this system. These therapies take advantage of the fact that the motor activity alters the properties of our neurons and muscles, including the pattern of their connectivity, and thus their functionality. Hence, a sensor-motor treatment where patients makes certain movements will help them (re)learn how to move the affected body parts. But these traditional rehabilitation processes are usually repetitive and lengthy, reducing motivation and adherence to the treatment, and thus limiting the benefits for the patients

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates

Kilorobot Search and Rescue Using an Immunologically Inspired Approach

This paper presents a new concept and simulated results for cooperatively coordinating autonomous robot teams via the Immunology-derived Distributed Autonomous Robotics Architecture (IDARA) to perform autonomous search and rescue operations. Primarily designed for the coordination and control of large-scale, kilorobot colonies, this architecture uses the unique stochastic learning and response mechanisms of the immune system as a basis to yield a more astute and adaptive response so that actions are varied from being reactionary to deliberative as indicated by environmental conditions and the architecture's perceived capabilities to address them. The IDARA architecture exhibits the guided stochastic search characteristics similar to those found in the human immune system. This characteristic was exploited to develop a series of methods for performing terrain search of dynamic environments. These methods were then evaluated in a variety of domains via computer simulations with robot colonies consisting of up to 1,500 robots. These experiments show that the IDARA architecture and framework provides a simple and robust method that is computationally efficient and does not degrade when coordinating and distributing large colonies of robots in either the terrain exploration and mapping or search and rescue problem domains. By providing new levels of scalability in noisy environments IDARA enables the full potential of micro-scale robotic for intelligent exploration, mapping, and search and rescue operations in a manner not afforded by traditional methods.