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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.

The ability to have unmanned ground vehicles navigate unmapped off-road terrain has high impact potential in application areas ranging from supply and logistics, to search and rescue, to planetary exploration. To achieve this, robots must be able to estimate the traversability of the terrain they are facing, in order to be able to plan a safe path through rugged terrain. In the work described here, we pursue the idea of fine-tuning a generic visual recognition network to our task and to new environments, but without requiring any manually labelled data. Instead, we present an autonomous data collection method that allows the robot to derive ground truth labels by attempting to traverse a scene and using localisation to decide if the traversal was successful. We then present and experimentally evaluate two deep learning architectures that can be used to adapt a pre-trained network to a new environment. We prove that the networks successfully adapt to their new task and environment from a relatively small dataset.

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