Tac2Structure: Object Surface Reconstruction Only through Multi Times Touch

Inspired by the ability of humans to perceive the surface texture of unfamiliar objects without relying on vision, the sense of tactile can play a crucial role in the process of robots exploring the environment, especially in some scenes where vision is difficult to apply or occlusion is inevitable to exist. Existing tactile surface reconstruction methods rely on external sensors or have strong prior assumptions, which will limit their application scenarios and make the operation more complex. This paper presents a surface reconstruction algorithm that uses only a new vision-based tactile sensor where the surface structure of an unfamiliar object is reconstructed by multiple tactile measurements. Compared with existing algorithms, the proposed algorithm doesn't rely on external devices and focuses on improving the reconstruction accuracy of the large-scale object surface. Aiming at the difficulty that the reconstruction accuracy is easily affected by the pressure of sampling, we propose a correction algorithm to adapt it. Multi-frame tactile imprints generated from many times contact can accurately reconstruct global object surface by jointly using the point cloud registration algorithm, loop-closure detection algorithm based on deep learning, and pose graph optimization algorithm. Experiments verify the proposed algorithm can achieve millimeter-level accuracy in reconstructing the surface of interactive objects and provide accurate tactile information for the robot to perceive the surrounding environment.

Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural Networks

Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are implemented to learn different new tasks, they usually require a significant amount of work on task-dependent fine-tuning. This paper aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand. More specifically, we provide a set of various local signals, a set of mathematical operators, and a global reward signal, after which a Cartesian genetic programming process finds an optimal learning rule from these components. Using this approach, we find learning rules that successfully solve an XOR and cart-pole task, and discover new learning rules that outperform the baseline rules from literature.