The increasing demand for applications in competitive fields, such as assisted living and aerial robots, drives contemporary research into the development, implementation and integration of power-constrained solutions. Although, deep neural networks (DNNs) have achieved remarkable performances in many robotics applications, energy consumption remains a major limitation. The paper at hand proposes a hybrid variation of the well-established deep deterministic policy gradient (DDPG) reinforcement learning approach to train a 6 degree of freedom robotic arm in the target-reach task https://www.youtube.com/watch?v=238693OpvD0. In particular, we introduce a spiking neural network (SNN) for the actor model and a DNN for the critic one, aiming to find an optimal set of actions for the robot. The deep critic network is employed only during training and discarded afterwards, allowing the deployment of the SNN in neuromorphic hardware for inference. The agent is supported by a combination of RGB and laser scan data exploited for collision avoidance and object detection. We compare the hybrid-DDPG model against a classic DDPG one, demonstrating the superiority of our approach.

In recent decades, modern developed societies have experienced a significant increase in life expectancy, resulting in a significant degree of aging in the population. This demographic shift has had a significant impact on the daily needs and habits of the population. According to recent demographic projections, the elderly population is expected to triple and reach 2 billion people worldwide by 2050. However, future societies around the world are not adequately prepared to face the potential challenges arising from population aging. Elderly people tend to spend more time at home, but the functionalities and ergonomics of current home equipment and appliances do not fully satisfy their needs. Moreover, the absence of familiarity with new, smarter, and automated domestic technologies creates a significant gap that affects the acceptance and adoption of these technologies by the elderly. In this work, we present the most important technical challenges for the integration of assisted living technologies into a Mobile Robotic Platform (MRP). Additionally, we present the ASPiDA concept, a project proposing a holistic system to support elderly people in their domicile environment.

Due to the wide spread of robotics technologies in everyday activities, from industrial automation to domestic assisted living applications, cutting-edge techniques such as deep reinforcement learning are intensively investigated with the aim to advance the technological robotics front. The mandatory limitation of power consumption remains an open challenge in contemporary robotics, especially in real-case applications. Spiking neural networks (SNN) constitute an ideal compromise as a strong computational tool with low-power capacities. This paper introduces a spiking neural network actor for a baseline robotic manipulation task using a dual-finger gripper. To achieve that, we used a hybrid deep deterministic policy gradient (DDPG) algorithm designed with a spiking actor and a deep critic network to train the robotic agent. Thus, the agent learns to obtain the optimal policies for the three main tasks of the robotic manipulation approach: target-object reach, grasp, and transfer. The proposed method has one of the main advantages that an SNN possesses, namely, its neuromorphic hardware implementation capacity that results in energy-efficient implementations. The latter accomplishment is highly demonstrated in the evaluation results of the SNN actor since the deep critic network was exploited only during training. Aiming to further display the capabilities of the introduced approach, we compare our model with the well-established DDPG algorithm.

Spiking Neural Networks (SNNs) have long been positioned as a biologically plausible and energy-efficient alternative to conventional deep learning models. Reinforcement Learning (RL), on the other hand, provides a framework for autonomous decision-making based on environmental interaction. While the convergence of SNNs and RL holds significant promise for robotics, this pathway remains in its infancy. This survey examines existing spiking RL approaches, categorizing them into bio-inspired models driven by reward-modulated local plasticity (e.g., R-STDP) and gradient-based models adapted from deep RL techniques. Additionally, we review neuron models, encoding schemes, and learning strategies, with emphasis on robotic applications. We further analyze evaluation metrics, emerging trends, limitations, and proposed future directions to bridge the gap between biological plausibility and scalable robotic intelligence. To the best of our knowledge, this work represents the first comprehensive survey specifically dedicated to RL with SNNs in robotic systems.

Feature discrimination is a crucial aspect in neural network design, as it directly impacts the network's ability to distinguish between classes and generalize across diverse datasets. The accomplishment of achieving high-quality feature representations ensures high intra-class separability and poses one of the most challenging research directions. While convetional deep neural networks (DNNs) rely on complex transformations and very deep networks to come up with meaningful feature representations, they usually require days of training and consume significant energy amounts. To this end, spiking neural networks (SNNs) offer a promising alternative. SNN's ability to capture temporal and spatial dependencies renders them particularly suitable for complex tasks, where multi-modal data are required. In this paper, we propose a feature discrimination approach for multi-modal learning with SNNs, focusing on audiovisual data. We employ deep spiking residual learning for visual modality processing and a simpler yet efficient spiking network for auditory modality processing. Lastly, we deploy a spiking multilayer perceptron for modality fusion. We present our findings and evaluate our approach against similar works in the field in classification challenges. To the best of our knowledge, this is the first work investigating feature discrimination in SNNs.

The mining sector increasingly adopts digital tools to improve operational efficiency, safety, and data-driven decision-making. One of the key challenges remains the reliable acquisition of high-resolution, geo-referenced spatial information to support core activities such as extraction planning and on-site monitoring. This work presents an integrated system architecture that combines UAV-based sensing, LiDAR terrain modeling, and deep learning-based object detection to generate spatially accurate information for open-pit mining environments. The proposed pipeline includes geo-referencing, 3D reconstruction, and object localization, enabling structured spatial outputs to be integrated into an industrial digital twin platform. Unlike traditional static surveying methods, the system offers higher coverage and automation potential, with modular components suitable for deployment in real-world industrial contexts. While the current implementation operates in post-flight batch mode, it lays the foundation for real-time extensions. The system contributes to the development of AI-enhanced remote sensing in mining by demonstrating a scalable and field-validated geospatial data workflow that supports situational awareness and infrastructure safety.