In this article, we propose an approach to extract variable-impedance during cutting tasks from human demonstrations, so as to ease soft-tissue cutting by robots. We model the dynamic adjustment of the human arm during interactions with the tissue and transfer these adaptive capabilities to the robot, by learning both the motion and change of impedance. To improve performance during task execution, our variable-impedance skill-transfer framework combines the learned model with an interactive-operation and feedback controller. To offer the flexibility of modifying the trajectory at run time, we use a control law based on dynamical systems. We couple this control law with virtual dynamics that describes the cutting dynamics. This ensures that the robot can control the interaction force and can plan the trajectory. The approach is validated in a real robot cutting-experiment of cutting different hardness tissues. Results show that our learning-based feedback controller, with the assistance from interactive-operation models, can effectively improve the task’s success rate, cutting length, cutting depth, and other indicators, as compared to using a fixed and variable-impedance gain controller.

The tiger beetle can maintain its stability by controlling the interaction force between its legs and an unstructured terrain while it runs. The biomimetic hexapod robot mimics a tiger beetle, and a comprehensive force sensing system combined with certain algorithms can provide force information that can help the robot understand the unstructured terrain that it interacts with. This study introduces a complicated leg force sensing system for a hexapod robot that is the same for all six legs. First, the layout and configuration of sensing system are designed according to the structure and sizes of legs. Second, the joint toque sensors, 3-DOF foot-end force sensor and force information processing module are designed, and the force sensor performance parameters are tested by simulations and experiments. Moreover, a force sensing system is implemented within the robot control architecture. Finally, the experimental evaluation of the leg force sensor system on the hexapod robot is discussed and the performance of the leg force sensor system is verified.

Compared with traditional in-pipe robots, tensegrity robots have exhibited many advantages such as light-weight, compliant, collapsible, low-cost, and rapidly manufacturable characteristics. However, published tensegrity in-pipe robots still have limited load capacity, because they rely on the stress between obliquely arranged rigid struts and pipe to provide supporting force, and the rigid struts are easy to slide under external loads. In this paper, a soft umbrella-shaped tensegrity robot that use compressed elastic struts to apply stress nearly perpendicular to pipe is proposed to enhance its load capacity. The static and kinematic models guiding the prototyping and controlling of the tensegrity robot are built based on discretization method. To assess the effectiveness of the elastic struts, a prototype is developed and subjected to a series of experiments. The results demonstrate that compared with other tensegrity in-pipe robots, the usage of elastic struts improves the payload-to-weight ratio of the proposed robot by three times, while still maintaining good mobility and adaptability.

A variable impedance skill transfer framework is introduced and experimentally evaluated in this paper. This framework mainly includes the following: 1. a real-time estimation algorithm of arm endpoint stiffness based on the human arm endpoint impedance model, and 2. the tele-impedance demonstration (tele-demonstration) method and Gaussian mixture model (GMM)-based learning from demonstration (LfD) method, which are designed according to the impedance model and the variable impedance skill transfer process. The modelling of the human arm endpoint impedance is inspired by human operation habits. The model can modify the task stiffness in three Cartesian directions based on the arm configuration and muscle co-contraction effect. The model parameters and their values are identified by performing a perturbation-based arm endpoint impedance identification experiment. The real-time impedance model is used for tele-demonstration and obtaining multimodality learning data, including human arm position and impedance information. These multimodality learning data are used for the variable impedance skill learning and generalization, and an electrical switch skill is learned and generalized by a compliant robotic arm in the experiments to execute a variable impedance interaction task in a dynamic uncertain environment.

In this research, properties of variable admittance controller and variable impedance controller were simulated by MATLAB firstly, which reflected the good performance of these two controllers under trajectory tracking and physical interaction. Secondly, a new mode of learning from demonstration (LfD) that conforms to human intuitive and has good interaction performances was developed by combining the electromyogram (EMG) signals and variable impedance (admittance) controller in dragging demonstration. In this learning by demonstration mode, demonstrators not only can interact with manipulator intuitively, but also can transmit end-effector trajectories and impedance gain scheduling to the manipulator for learning. A dragging demonstration experiment in 2D space was carried out with such learning mode. Experimental results revealed that the designed human-robot interaction and demonstration mode is conducive to demonstrators to control interaction performance of manipulator directly, which improves accuracy and time efficiency of the demonstration task. Moreover, the trajectory and impedance gain scheduling could be retained for the next learning process in the autonomous compliant operations of manipulator.