Intelligence in robotics for computer, engineering, and applied sciences

Abstract

Intelligence in robotics for computer, engineering, and applied sciencesIntelligence in robotics is an extension of automation that increases the robot's intelligence with continuous learning and adaptation to helping humans more effectively.Intelligence in robotics in a real-world setting consists of intelligence in robot action or body motion, with manipulation, mobility, structural, and human-robot interaction intelligence.For instance, the robot could employ the structural intelligence to modify its configuration, use the manipulation intelligence to learn new skills during operation, develop the mobility intelligence to move independently, and utilize the human-robot interaction intelligence to interact with humans in an intuitive manner based on their intentions.Intelligence in robotics is based on computation, with learning as its core-this allows robots to mimic human behavior and to adapt to various situations.For instance, a robot vision mimics human sense of the surroundings, a robot decision-making mimics human management of tasks in a variety of situations, and a robot skill learning mimics human learning and creation.To this end, this special issue brings together cutting-edge research findings, technological innovations, and innovative conceptual frameworks related to developing fields of robotics intelligence, including manipulation learning, bio-inspired robotics, autonomous robotics, human-robot interaction, and related fields and applications.This special issue contains 18 papers, describing the latest advances on intelligence in robotics, disseminating the topic of intelligence in robotics to various aspects.The following is a brief summary of each paper's primary contributions.Robot-assisted rehabilitation is an efficient utilization of the assist-as-needed (AAN) controller.In the "Bayesian optimization for assist-as-needed controller in the robot-assisted upper limb training based on energy information" by Zhang et al. [1], an adaptive AAN has been proposed to motivate subjects' participation by assigning them a personalized assistance level according to their performance and engagement estimation throughout trials.Energy-related information was utilized to estimate the engagement, and the Bayesian optimization was subsequently applied to the AAN controller to enhance the participant's performance based on a prior trial-wise performance.This minimizes the average trajectory error and reduces the energy consumption.Estimating contact parameters (slippage and sinkage) is an important challenge for a robot moving on an uneven terrain.An in-depth description of contact parameters for mobile robots has been presented in "A contact parameter estimation method for multi-modal robot locomotion on deformable granular terrains" by Lyu et al. [2].Compared with other direct contact measurement techniques intended for different motion modes, this convolutional neural network (CNN) and discrete wavelet transformationbased approach can not only precisely predict the contact parameters of multi-modal robot moving on an uneven terrain but also obtain a comparable or more effective performance.Physically compliant actuators offer a multitude of advantages for robots, including heightened environmental suitability, enhanced human-robot interactions, and greater energy efficiency.These advantages stem from the inherent compliance of the actuators.In "Design and control of a compliant robotic actuator with parallel spring-damping transmission" by Yuan et al. [3], the effect of incorporating variable damping into a compliant actuator was examined.The proposed structural design, which features a variable damping element in parallel with a common series elastic actuator (SEA), achieves an improved stability and dynamic performance in the force and position control.