Human-robot Cooperative Manipulation Research Articles

Adaptive Cooperative Control for Human-Robot Load Manipulation

In this letter, we propose a control strategy for human-robot cooperative manipulation under the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ambiguous</i> collaboration of a human agent. To cope with this uncertainty, an adaptive update law inferring the human contribution to the system dynamics from basic perception feedback through the human arm stiffness is used. Furthermore, the robustness and accuracy of the approach is enhanced by redundantly tracking the shared load references and its associated end-effector position references. To validate the control strategy, both theoretical Lyapunov stability analysis and experimental results –employing two robot manipulators with 6 degrees of freedom under external disturbances– are provided.

Skill Learning Strategy Based on Dynamic Motion Primitives for Human–Robot Cooperative Manipulation

This article presents a skill learning-based hierarchical control strategy for human-robot cooperative manipulation, which constitutes a novel learning-control system. The high-level learning strategy aims to learn the motor skills from human demonstrations by fusion with dynamic motion primitives (DMPs) and the Gaussian mixture model (GMM). The lower level control strategy guarantees the compliance of the robot movement under human interaction using admittance control and integral barrier Lyapunov function (IBLF)-based adaptive neural controller. First, the robot learns the motor skills from observing the successful execution of tasks by a demonstrator through DMP-GMM methods. Then, the robot reproduces the complex skills and executes the interactive task by demonstrations. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with experimental results. The results show that the developed hierarchical strategy has good performance in cooperation by learning and control that reacts compliantly to robot interaction with human subjects.

A Learning-Based Hierarchical Control Scheme for an Exoskeleton Robot in Human-Robot Cooperative Manipulation.

Exoskeleton robots can assist humans to perform activities of daily living with little effort. In this paper, a hierarchical control scheme is presented which enables an exoskeleton robot to achieve cooperative manipulation with humans. The control scheme consists of two layers. In low-level control of the upper limb exoskeleton robot, an admittance control scheme with an asymmetric barrier Lyapunov function-based adaptive neural network controller is proposed to enable the robot to be back drivable. In order to achieve high-level interaction, a strategy for learning human skills from demonstration is proposed by utilizing Gaussian mixture models, which consists of the learning and reproduction phase. During the learning phase, the robot observes and learns how a demonstrator performs a specific impedance-based task successfully, and in the reproduction phase, the robot can provide the subjects with just enough assistance by extracting human skills from demonstrations to prevent the motion of the robot end-effector deviating far from desired ones, due to variation in the interaction force caused by environmental disturbances. Experimental results of two different tasks show that the proposed control scheme can provide human subjects with assistance as needed during cooperative manipulation.

Interactive Plan Execution during Human-Robot Cooperative Manipulation

Abstract Collaborative robots (Cobots) are robotic systems designed to physically interact with humans during task execution in a shared industrial workspace. In this paper, we consider a scenario in which a human operator can be supported by a lightweight robotic arm in order to accomplish complex manipulation tasks. Specifically, we assume that manipulation tasks are explicitly represented as hierarchical task networks to be interactively executed exploiting the human physical guidance. In this setting, the human interventions are continuously interpreted by the robotic system in order to infer whether the human guidance is aligned or not with respect to the planned activities. The interpreted human interventions are then exploited by the robotic system to adapt its cooperative behavior during the execution of the shared plan. Depending on the estimated operator intentions, the robotic system can adjust tasks or motions, while regulating the robot compliance in order to follow or lead the human co-worker. The proposed approach is demonstrated in a testing scenario consisting of a human operator that interacts with a Kuka LBR iiwa arm in order to perform a cooperative manipulation task. The collected experimental results show the feasibility and the effectiveness of the approach.

Reducing the human effort for human-robot cooperative object manipulation via control design

This study is concerned with the shared object manipulation problem in a physical Human-Robot Interaction (pHRI) setting. In such setups, the operator manipulates the object with the help of a robot. In this paper, the operator is assigned with the lead role, and the robot is passively following the forces/torques exerted by the operator. We propose a controller that is free from the well-known translation/rotation problem and enhances the operator’s ability to move the object by reducing the human effort. The key point in our study is that the controller is defined based on the instantaneous center of rotation. The passivity of the system including the object and the manipulator has been evaluated. Simulation results validate the theoretical findings on different scenarios of subsequent rotations and translations of the object.

Human-Robot Cooperative Manipulation with Motion Estimation Using Minimum-Jerk Model.

In this paper, a control method of robots for human-robot cooperative manipulation is investigated. We propose estimating human motion using the minimum jerk model for smooth cooperation. Using nonlinear least-squares method, we identify two parameters of the minimum-jerk model in real-time. The estimated position of the human hand is used to determine the desired position of the end-effector of the manipulator in virtual compliance control. The motion estimation enables robots to coordinate actively even for unknown trajectories of handled objects that human partners intend. We implemented the proposed method on an industrial 6-degree-of-freedom manipulator with a force sensor. In experiments of cooperative manipulation of a rubber pipe, the motion estimation improved human feeling in coordination. The improvement was quantitatively evaluated from the viewpoint of unnecessary energy transfer.