This letter presents a novel teleoperation interface that enables remote loco-manipulation control of a MObile Collaborative robotic Assistant (MOCA). MOCA is a new research platform developed at the Istituto Italiano di Tecnologia (IIT), which is composed of a lightweight manipulator arm, a Pisa/IIT SoftHand, and a mobile platform driven by four omni-directional wheels. A whole-body impedance controller is consequently developed to ensure accurate tracking of the impedance and position trajectories at MOCA end-effector by considering the causal interactions in such a dynamic system. The proposed teleoperation interface provides the user with two control modes: locomotion and manipulation. The locomotion mode receives inputs from a personalized human center-of-pressure model, which enables real-time navigation of the MOCA mobile base in the environment. The manipulation mode receives inputs from a tele-impedance interface, which tracks human arm endpoint stiffness and trajectory profiles in real time and replicates them using the MOCA's whole-body impedance controller. To evaluate the performance of the proposed teleoperation interface in the execution of remote tasks with dynamic uncertainties, a sequence of challenging actions, i.e., navigation, door opening, and wall drilling, has been considered in the experimental setup.

This paper proposes an improved disturbance observer to realize accurate contact force estimation using the joint torque sensor. The joint torque sensor separates the dynamics of the link side from the motor side of the robot manipulator. Therefore, only computing the partial dynamics on the link side can realize external force estimation. This can considerably reduce the modeling workload and error terms that may affect the estimation results. Furthermore, this paper presents that the observed residual value during free motion can be considered as the friction dynamics, which is approximated by the neural network (NN) due to its inherent capacity in approximating nonlinear functions. After that, the estimation accuracy of the observer is considerably improved. Compared to other local NN approximation method, we analyzes the properties of the friction force in detail to select appropriate excitation trajectory for accurate global approximation results using only limited training data. We have presented that the suitable excitation trajectory and the use of global basis function are the sufficient conditions for global friction approximation. The proof of this theorem is also given. The Kalman filter is also utilized to reduce the noise of the estimation results in real-time. The experimental results also demonstrate the efficacy of the proposed method, which accurately estimates the contact force for flexible joint robots.

In this work, we propose a framework to address the autonomous impedance regulation problem of robots in a class of constrained manipulation tasks. In this framework, a human arm endpoint stiffness model is used to extract the task stiffness geometry along the constrained trajectory, which is then encoded offline and reproduced online by a Gaussian Mixture Model (GMM) and the Gaussian Mixture Regression (GMR), respectively. Furthermore, the full Cartesian impedance of the robot is formulated through an optimal control problem, i.e., the Linear-Quadratic Regulator (LQR), in which the task stiffness geometry (extracted from human demonstrations) is considered as the time-varying weighting matrix Q. The optimal impedance is eventually realised by the robot through a task geometry consistent Cartesian impedance controller. A tank-based passivity observer is implemented to give evidence on the stability of the system during online impedance variations. To evaluate the performance of the framework, a comparative experiment with three different impedance settings (i.e., the proposed framework, the framework without LQR and the framework without GMM/GMR) for Franka Emika Panda to perform a door opening task was conducted. The results reveal that our framework outperforms the other two, in terms of tracking error and the interaction forces.

This work presents a unified approach for hybrid motion control of the Mobile Collaborative Robotic Assistant (MOCA). The objective is to develop a loco-manipulation controller, enabling various couplings of the arm and the mobile base movements, and particularly their purely decoupled motions. The proposed method is based on a weighted whole-body Cartesian impedance controller, where the decoupling of the motions can be achieved by solving the local optimization problem of the weighted joint torques in the first task space and its nullspace, respectively. Under this control framework, by tuning the weighting terms and a nullspace gain, three motion modes, i.e. Locomotion, Manipulation, and Modified Loco-Manipulation, are implemented. To evaluate the proposed approach, a door opening task that requires different mobility patterns of the arm, the mobile base, and their coupled movements is demonstrated. The experiment results validate the proposed methodology and provide a comprehensive understanding of the differences among the above motion modes.

Traditional skill transfer frameworks mainly focus on kinematics, while there has been a lack of studies on dynamics. This article develops a Riemannian-based dynamic movement primitive (DMP) framework for learning and generalizing multispace data, including position, orientation, and stiffness from human demonstration. A simplified geometric configuration of the human arm skeleton is adopted to extract its endpoint stiffness. The dynamic skills of a variable stiffness are obtained in real time and then transferred to robots. The effectiveness of the presented approach is verified by two experiments in real scenarios on the Franka Emika Panda robot. The experimental results indicate that both kinematic and dynamic skills can be learned and generalized by the extended DMP framework with high accuracy and strong correlation. The human-like variable impedance control of a robot can be successfully realized by using the proposed approach. Thus, the proposed approach, including the stiffness estimation model and the skill learning and generalization framework, is suitable for applications of human–robot collaboration, contact operation, and teleoperation, where the position, orientation, and stiffness need to be considered simultaneously.

In this work, we propose an intuitive and real-time model of the human arm active endpoint stiffness. In our model, the symmetric and positive-definite stiffness matrix is constructed through the eigendecomposition ${\mathit{K}}_c=\mathit{V}\mathit{D}{\mathit{V}}^T$, where $\mathit{V}$ is an orthonormal matrix whose columns are the normalized eigenvectors of ${\mathit{K}}_c$, and $\mathit{D}$ is a diagonal matrix whose entries are the eigenvalues of ${\mathit{K}}_c$. In this formulation, we propose to construct $\mathit{V}$ and $\mathit{D}$ directly by exploiting the geometric information from a reduced human arm skeleton structure in 3D and from the assumption that human arm muscles work synergistically when co-contracted. Through the perturbation experiments across multiple subjects under different arm configurations and muscle activation states, we identified the model parameters and examined the modeling accuracy. In comparison to our previous models for predicting human active arm endpoint stiffness, the new model offers significant advantages such as fast identification and personalization due to its principled simplicity. The proposed model is suitable for applications such as teleoperation, human–robot interaction and collaboration, and human ergonomic assessments, where a personalizable and real-time human kinodynamic model is a crucial requirement.