Artificial nociception and motor responses to pain, for humans and robots

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

Introduction

Pain is a crucial function for organisms. Building a "Robot Pain" model inspired by organisms' pain could help the robot learn self-preservation and extend longevity. Most previous studies about robots and pain focus on robots interacting with people by recognizing their pain expressions or scenes, or avoiding obstacles by recognizing dangerous objects. Robots do not have human-like pain capacity and cannot adaptively respond to danger. Inspired by the evolutionary mechanisms of pain emergence and the Free Energy Principle (FEP) in the brain, we summarize the neural mechanisms of pain and construct a Brain-inspired Robot Pain Spiking Neural Network (BRP-SNN) with spike-time-dependent-plasticity (STDP) learning rule and population coding method.

Methods

The proposed model can quantify machine injury by detecting the coupling relationship between multi-modality sensory information and generating "robot pain" as an internal state.

Results

We provide a comparative analysis with the results of neuroscience experiments, showing that our model has biological interpretability. We also successfully tested our model on two tasks with real robots-the alerting actual injury task and the preventing potential injury task.

Discussion

Our work has two major contributions: (1) It has positive implications for the integration of pain concepts into robotics in the intelligent robotics field. (2) Our summary of pain's neural mechanisms and the implemented computational simulations provide a new perspective to explore the nature of pain, which has significant value for future pain research in the cognitive neuroscience field.

Arm movement adaptation to concurrent pain constraints

How do humans coordinate their movements in order to avoid pain? This paper investigates a motor task in the presence of concurrent potential pain sources: the arm must be withdrawn to avoid a slap on the hand while avoiding an elbow obstacle with an electrical noxious stimulation. The results show that our subjects learned to control the hand retraction movement in order to avoid the potential pain. Subject-specific motor strategies were used to modify the joint movement coordination to avoid hitting the obstacle with the elbow at the cost of increasing the risk of hand slap. Furthermore, they used a conservative strategy as if assuming an obstacle in 100% of the trials.