Robots that anticipate pain: Anticipating physical perturbations from visual cues through deep predictive models

Abstract

To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge about sources of perturbation to be encoded before deployment, our method is based on experiential learning. Robots learn to associate visual cues with subsequent physical perturbations and contacts. In turn, these extracted visual cues are then used to predict potential future perturbations acting on the robot. To this end, we introduce a novel deep network architecture which combines multiple subnetworks for dealing with robot dynamics and perceptual input from the environment.We present a self-supervised approach for training the system that does not require any labeling of training data. Extensive experiments in a human-robot interaction task show that a robot can learn to predict physical contact by a human interaction partner without any prior information or labeling. Furthermore, the network is able to successfully predict physical contact from either depth stream input or traditional video input or using both modalities as input.

A brain-inspired robot pain model based on a spiking neural network

“…Kuehn and Haddadin ( 2016 ) designed the robot's neural reflex behavior for pain, which is only a pain-induced avoidance response and not a human-like pain capacity. Sur and Amor ( 2017 ) attempted to model the robot's pain capacity, but the network structure they used is different from the biological mechanism. We explored the neural mechanisms of pain and established a Brain-inspired Robot Pain Spiking Neural Network (BRP-SNN) to simulate the brain regions involving pain, using the spike-timing-dependent-plasticity (STDP) learning rule to train the connection weights.…”

“…This phenomenon is called pain memory (Eich et al, 1985 ). Therefore, pain is a passive response to actual physical injury and active response to potential physical injury (Sur and Amor, 2017 ). We argue that the Robot Pain should be designed in line with the neural mechanism of pain mentioned above—it should first respond to actual machine injury and then respond to potential machine injury.…”