How can we build robot controllers that are able to work under harsh conditions, but without experiencing catastrophic failures? As seen on the recent Fukushima's nuclear disaster, standard robots break down when exposed to high radiation environments. Here we present the results from two arrangements of Spiking Neural Networks, based on the Liquid State Machine (LSM) framework, that were able to gracefully degrade under the e↵ects of a noisy current injected directly into each simulated neuron. These noisy currents could be seen, in a simplified way, as the consequences of exposition to non-destructive radiation. The results show that not only can the systems withstand noise, but one of the configurations, the Modular Parallel LSM, actually improved its results, in a certain range, when the noise levels were increased. Also, the robot controllers implemented in this work are suitable to run on a modern, power e cient neuromorphic hardware such as SpiNNaker.

This paper presents a proof-of-concept platform for demonstrating robotic harvesting of summer-varieties of cauliflower, and early tests performed under laboratory conditions. The platform is designed to be modular and has two dexterous robotic arms with variablestiffness technology. The bi-manual configuration enables the separation of grasping and cutting behaviours into separate robot manipulators. By exploiting the passive compliance of the variable-stiffness arms, the system can operate with both grasping and cutting tool close to the ground. Multiple 3D vision cameras are used to track the cauliflowers in real-time, and to attempt to assess the maturity. Early experiments with the platform in the laboratory highlight the potential and challenges of the platform.

Biological neural networks are able to control limbs in different scenarios, with high precision and robustness.As neural networks in living beings communicate through spikes, modern neuromorphic systems try to mimic them making use of spike-based neuron models. Liquid State Machines (LSM), a special type of Reservoir Computing system made of spiking units, when it was first introduced, had plasticity on an external layer and also through Short-Term Plasticity (STP) within the reservoir itself. However, most neuromorphic hardware currently available does not implement both Short-Term Depression and Facilitation and some of them don't support STP at all. In this work, we test the impact of STP in an experimental way using a 2 degrees of freedom simulated robotic arm controlled by an LSM. Four trajectories are learned and their reproduction analysed with Dynamic Time Warping accumulated cost as the benchmark. The results from two different set-ups showed the use of STP in the reservoir was useful for one out of three tested trajectories, though not computationally cost-effective for this particular robotic task.

In this paper we aim for the replication of a state of the art architecture for recognition of human actions using skeleton poses obtained from a depth sensor. We review the usefulness of accurate human action recognition in the field of robotic elderly care, focusing on fall detection. We attempt fall recognition using a chained Growing When Required neural gas classifier that is fed only skeleton joints data. We test this architecture against Recurrent SOMs (RSOMs) to classify the TST Fall detection database ver. 2, a specialised dataset for fall sequences. We also introduce a simplified mathematical model of falls for easier and faster bench-testing of classification algorithms for fall detection. The outcome of classifying falls from our mathematical model was successful with an accuracy of 97.12 ± 1.65 % and from the TST Fall detection database ver. 2 with an accuracy of 90.2 ± 2.68 % when a filter was added.

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