Bodily aware soft robots: integration of proprioceptive and exteroceptive sensors
This addresses the unsolved issue of bodily awareness in robots, which is crucial for tasks like moving in darkness or grasping objects, though it appears incremental as it builds on existing sensor integration and neural network methods.
The paper tackles the problem of robotic bodily awareness by integrating exteroceptive and proprioceptive sensors, using a stacked convolutional autoencoder and recurrent neural network to enable a simulated soft robot to imagine its motion without visual input.
Being aware of our body has great importance in our everyday life. This is the reason why we know how to move in a dark room or to grasp a complex object. These skills are important for robots as well, however, robotic bodily awareness is still an unsolved problem. In this paper we present a novel method to implement bodily awareness in soft robots by the integration of exteroceptive and proprioceptive sensors. We use a combination of a stacked convolutional autoencoder and a recurrent neural network to map internal sensory signals to visual information. As a result, the simulated soft robot can learn to \textit{imagine} its motion even when its visual sensor is not available.