👻Soft-bodied robots with DL-neural networks from MIT researchers
Traditional robots, hard and metal, are not suitable for all tasks. So scientists try to create flexible and soft robots that can safely interact with people and easily enter confined spaces. But these robots need to know the location of all parts of its body, which can change in any configuration.
Due to the limited range of motion due to a given set of joints and limbs, rigid robots are perfectly controllable using algorithms that map and plan their movement. The problem with soft robots is that the space of their deformations and movements is practically infinite. Of course, you can determine the position of the robot using a video camera and simply transmit this information to the control program. But it is dependence on an external device (camera) appears. Therefore, in order to determine the optimal number of sensors and their most efficient placement on the robot itself, MIT researchers have developed a new neural network architecture. The ML-algorithm of deep learning optimizes the placement of sensors by data about the deformation of different parts of the robot's body during its movement when performing applied tasks, for example, grabbing objects.
This ML algorithm performed better in test simulation compared to expert predictions of robototechicians on robots with touch screens and touch controls.
https://news.mit.edu/2021/sensor-soft-robots-placement-0322