Biological systems, including human beings, have the innate ability to perform complex tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to comprehend and formally define this innate characteristic. The idea, supported by several experimental findings, that biological systems are able to combine and adapt basic units of motion into complex tasks finally leads to the formulation of the motor primitives’ theory. In this respect, Dynamic Movement Primitives (DMPs) represent an elegant mathematical formulation of the motor primitives as stable dynamical systems and are well suited to generate motor commands for artificial systems like robots. In the last decades, DMPs have inspired researchers in different robotic fields including imitation and reinforcement learning, optimal control, physical interaction, and human–robot co-working, resulting in a considerable amount of published papers. The goal of this tutorial survey is two-fold. On one side, we present the existing DMP formulations in rigorous mathematical terms and discuss the advantages and limitations of each approach as well as practical implementation details. In the tutorial vein, we also search for existing implementations of presented approaches and release several others. On the other side, we provide a systematic and comprehensive review of existing literature and categorize state-of-the-art work on DMP. The paper concludes with a discussion on the limitations of DMPs and an outline of possible research directions.

Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approach in robotics to avoid large impact forces while operating in unstructured environments. In such environments, the conditions under which the interaction occurs may significantly vary during the task execution. This demands robots to be endowed with online adaptation capabilities to cope with sudden and unexpected changes in the environment. In this context, variable impedance control arises as a powerful tool to modulate the robot's behavior in response to variations in its surroundings. In this survey, we present the state-of-the-art of approaches devoted to variable impedance control from control and learning perspectives (separately and jointly). Moreover, we propose a new taxonomy for mechanical impedance based on variability, learning, and control. The objective of this survey is to put together the concepts and efforts that have been done so far in this field, and to describe advantages and disadvantages of each approach. The survey concludes with open issues in the field and an envisioned framework that may potentially solve them.

As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills to robots, where human demonstrations represented in Cartesian or joint spaces are utilized to estimate task/skill models that can be subsequently generalized to new situations. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in many others. Despite recent advances in learning orientations from demonstrations, several crucial issues have not been adequately addressed yet. For instance, how can demonstrated orientations be adapted to pass through arbitrary desired points that comprise orientations and angular velocities? In this article, we propose an approach that is capable of learning multiple orientation trajectories and adapting learned orientation skills to new situations (e.g., via-points and end-points), where both orientation and angular velocity are considered. Specifically, we introduce a kernelized treatment to alleviate explicit basis functions when learning orientations, which allows for learning orientation trajectories associated with high-dimensional inputs. In addition, we extend our approach to the learning of quaternions with angular acceleration or jerk constraints, which allows for generating smoother orientation profiles for robots. Several examples including experiments with real 7-DoF robot arms are provided to verify the effectiveness of our method.

Soft robotic hands and grippers are increasingly attracting attention as robotic end effectors. Compared with their rigid counterparts, they are safer for human-robot and environment-robot interactions, easier to control, and more compliant, and they cost and weigh less. Design studies of soft robotic hands have focused mostly on the soft fingers and bending actuators. However, the palm is also an essential part in grasping. In this work, we propose a novel design for an inexpensive soft humanoid hand with pneumatic soft fingers and a soft palm. The configuration of the soft palm is based on a modular design that can be applied to actuate different kinds of existing soft fingers. The splaying of the fingers, bending of the whole palm, and abduction and adduction of the thumb are implemented by the palm. Moreover, we present a new design of soft finger, called the hybrid-bending soft finger (HBSF), that can both bend in the grasping axis and deflect in the side-to-side axis, achieving human-like motion (Figure 1). The functions of the HBSF and soft palm are evaluated both in simulation, using the Simulation Open Framework Architecture (SOFA) framework, and experimentally. Six finger designs with 1-11 longitudinal segments are analyzed. The versatility of the soft hand is evaluated and demonstrated experimentally by its grasping of objects according to Feix's taxonomy. The results demonstrate a great diversity of grasps, with 31 of the 33 grasp types of the taxonomy performed successfully with the proposed design, showing promise for grasping a large variety of objects with different shapes and weights.