Dance Teaching by a Robot: Combining Cognitive and Physical Human-Robot Interaction for Supporting the Skill Learning Process

This letter presents a physical human-robot interaction scenario in which a robot guides and performs the role of a teacher within a defined dance training framework. A combined cognitive and physical feedback of performance is proposed for assisting the skill learning process. Direct contact cooperation has been designed through an adaptive impedance-based controller that adjusts according to the partner's performance in the task. In measuring performance, a scoring system has been designed using the concept of progressive teaching (PT). The system adjusts the difficulty based on the user's number of practices and performance history. Using the proposed method and a baseline constant controller, comparative experiments have shown that the PT presents better performance in the initial stage of skill learning. An analysis of the subjects' perception of comfort, peace of mind, and robot performance have shown a significant difference at the p < .01 level, favoring the PT algorithm.Comment: Presented at IEEE International Conference on Robotics and Automation ICRA-201

Unpowered Knee Exoskeleton Reduces Quadriceps Activity during Cycling

Cycling is an eco-friendly method of transport and recreation. With the intent of reducing the energy cost of cycling without providing an additional energy source, we have proposed the use of a torsion spring for knee-extension support. We developed an exoskeleton prototype using a crossing four-bar mechanism as a knee joint with an embedded torsion spring. This study evaluates the passive knee exoskeleton using constant-power cycling tests performed by eight healthy male participants. We recorded the surface electromyography over the rectus femoris muscles of both legs, while the participants cycled at 200 and 225 W on a trainer with the developed wheel-accelerating system. We then analyzed these data in time–frequency via a continuous wavelet transform. At the same cycling speed and leg cadence, the median power spectral frequency of the electromyography increases with cycling load. At the same cycling load, the median power spectral frequency decreases when cycling with the exoskeleton. Quadriceps activity can be relieved despite the exoskeleton consuming no electrical energy and not delivering net-positive mechanical work. This fundamental can be applied to the further development of wearable devices for cycling assistance. Keywords: Augmentation, Cycling, Energy cost, Electromyography, Exoskeleton, Knee, Orthosis, Muscle activit

A Human-Following Motion Planning and Control Scheme for Collaborative Robots Based on Human Motion Prediction

Human–Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist human workers in their tasks and improve their efficiency. However, the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC)-based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker

Point Pair Feature-Based Pose Estimation with Multiple Edge Appearance Models (PPF-MEAM) for Robotic Bin Picking

Automation of the bin picking task with robots entails the key step of pose estimation, which identifies and locates objects so that the robot can pick and manipulate the object in an accurate and reliable way. This paper proposes a novel point pair feature-based descriptor named Boundary-to-Boundary-using-Tangent-Line (B2B-TL) to estimate the pose of industrial parts including some parts whose point clouds lack key details, for example, the point cloud of the ridges of a part. The proposed descriptor utilizes the 3D point cloud data and 2D image data of the scene simultaneously, and the 2D image data could compensate the missing key details of the point cloud. Based on the descriptor B2B-TL, Multiple Edge Appearance Models (MEAM), a method using multiple models to describe the target object, is proposed to increase the recognition rate and reduce the computation time. A novel pipeline of an online computation process is presented to take advantage of B2B-TL and MEAM. Our algorithm is evaluated against synthetic and real scenes and implemented in a bin picking system. The experimental results show that our method is sufficiently accurate for a robot to grasp industrial parts and is fast enough to be used in a real factory environment