Learning-based methods have demonstrated clear advantages in controlling robot tasks, such as the information fusion abilities, strong robustness, and high accuracy. Meanwhile, the on-board systems of robots have limited computation and energy resources, which are contradictory with state-ofthe-art learning approaches. They are either too lightweight to solve complex problems or too heavyweight to be used for mobile applications. On the other hand, training spiking neural networks (SNNs) with biological plausibility has great potentials of performing fast computation and energy efficiency. However, the lack of effective learning rules for SNNs impedes their wide usage in mobile robot applications. This paper addresses the problem by introducing an end to end learning approach of spiking neural networks for a lane keeping vehicle. We consider the reward-modulated spike-timing-dependentplasticity (R-STDP) as a promising solution in training SNNs, since it combines the advantages of both reinforcement learning and the well-known STDP. We test our approach in three scenarios that a Pioneer robot is controlled to keep lanes based on an SNN. Specifically, the lane information is encoded by the event data from a neuromorphic vision sensor. The SNN is constructed using R-STDP synapses in an all-to-all fashion. We demonstrate the advantages of our approach in terms of the lateral localization accuracy by comparing with other state-ofthe-art learning algorithms based on SNNs.
Deep neural networks (DNNs) set the benchmark in many tasks in perception and control. Spiking versions of DNNs, implemented on neuromorphic hardware can enable orders of magnitude lower power consumption and low latency during network use. In this paper, we explore behavior and generalization capability of spiking, quantized spiking, and hardware implementation of deep Qnetworks in two classical reinforcement learning tasks. We found that spiking neural networks have slightly decreased performance compared to non-spiking network, but we can avoid performance degradation from quantization and in-chip implementation. We conclude that since hardware implementation leads to lower power consumption and low latency, neuromorphic approach is a promising avenue for deep Q-learning. Furthermore, online learning, enabled in neuromorphic chips, can be used to compensate for the performance decrease in environments with parameter variations. CCS CONCEPTS• Computing methodologies → Reinforcement learning.
The energy efficiency of buildings drives the replacement of traditional construction materials with lightweight insulating materials. However, energy-efficient but combustible insulation might contribute to the building’s fire load. Therefore, it is necessary to analyse the reaction-to-fire properties of various insulating materials to provide a better understanding of designing a fire-safe structure. In this study, reaction-to-fire tests were carried out to assess the fire behaviour of lightweight polystyrene insulating panels commonly employed in high-rise buildings. The flammability characteristics of expanded polystyrene (EPS) and extruded polystyrene (XPS) were determined using a cone calorimeter under two distinct external irradiance regimes, 35 kW/m2 and 50 kW/m2, to approximate small to medium fire exposure situations. To investigate the effect of a fire-rated (FR) foil layer on a sandwich panel, three distinct test configurations were used: (i) sample without FR layer (standard sample), (ii) sample with FR layer (FR foil), and (iii) damaged layer (foil and vent) for EPS. Except for the smoke toxicity index (STI), the overall fire performance of EPS is superior to that of XPS. The findings of this study are useful in analysing fire performance and fire safety design for lightweight insulation panels.
The Human Brain Project (HBP) is a European Commission Future and Emerging Technologies Flagship Program 1 intended to advance our understanding of the brain. It is designed to run for a period of 10 years with strong collaboration between 116 partnering organizations from all over Europe. With total planned funding of roughly one billion Euros, HBP is a large effort with very complex problems and goals. As such, HBP effort is divided into 12 unique but interconnected research areas (so-called subprojects, see Fig. 1) designed to foster interdisciplinary collaboration between research laboratories across Europe [1].Experts in the fields of neuroscience, biology, medicine, computation, robotics, and many more combine their efforts towards common goals. HBP researchers have published over 250 academic papers since the program's inception in 2013, and many exciting research efforts are underway. This article will focus on the Neurorobotics subproject, which is led by Technical University of Munich and is intended to place computational brain models within virtual robotic bodies that can interact within simulated environments. Imagine a computational brain model being able to sense -see or feel -the world around it and richly interact -through movement or manipulation. The goal of the subproject is to help understand the concept of neurorobotics from computational neuroscience to intelligent robots and back.
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