Port-Hamiltonian modeling for soft-finger manipulation

In this paper, we present a port-Hamiltonian model of a multi-fingered robotic hand, with soft-pads, while grasping and manipulating an object. The algebraic constraints of the interconnected systems are represented by a geometric object, called Dirac structure. This provides a powerful way to describe the non-contact to contact transition and contact viscoelasticity, by using the concepts of energy flows and power preserving interconnections. Using the port based model, an Intrinsically Passive Controller (IPC) is used to control the internal forces. Simulation results validate the model and demonstrate the effectiveness of the port-based approach

Variable Impedance Control of Redundant Manipulators for Intuitive Human–Robot Physical Interaction

This paper presents an experimental study on human-robot comanipulation in the presence of kinematic redundancy. The objective of the work is to enhance the performance during human-robot physical interaction by combining Cartesian impedance modulation and redundancy resolution. Cartesian impedance control is employed to achieve a compliant behavior of the robot's end effector in response to forces exerted by the human operator. Different impedance modulation strategies, which take into account the human's behavior during the interaction, are selected with the support of a simulation study and then experimentally tested on a 7-degree-of-freedom KUKA LWR4. A comparative study to establish the most effective redundancy resolution strategy has been made by evaluating different solutions compatible with the considered task. The experiments have shown that the redundancy, when used to ensure a decoupled apparent inertia at the end effector, allows enlarging the stability region in the impedance parameters space and improving the performance. On the other hand, the variable impedance with a suitable modulation strategy for parameters' tuning outperforms the constant impedance, in the sense that it enhances the comfort perceived by humans during manual guidance and allows reaching a favorable compromise between accuracy and execution time

Redundancy resolution in human-robot co-manipulation with cartesian impedance control

In this paper the role of redundancy in Cartesian impedance control of a robotic arm for the execution of tasks in co-manipulation with humans is considered. In particular, the problem of stability is experimentally investigated. When a human operator guides the robot through direct physical interaction, it is desirable to have a compliant behaviour at the end effector according to a decoupled impedance dynamics. In order to achieve a desired impedance behaviour, the robot’s dynamics has to be suitably reshaped by the controller. Moreover, the stability of the coupled human-robot system should be guaranteed for any value of the impedance parameters within a prescribed region. If the robot is kinematically or functionally redundant, also the redundant degrees of freedom can be used to modify the robot dynamics. Through an extensive experimental study on a 7-DOF KUKA LWR4 arm, we compare two different strategies to solve redundancy and we show that, when redundancy is exploited to ensure a decoupled apparent inertia at the end effector, the stability region in the parameter space becomes larger. Thus, better performance can be achieved by using, e.g., variable impedance control laws tuned to human intentions

Learning Grasps in a Synergy-based Framework

In this work, a supervised learning strategy has been applied in conjunction with a control strategy to provide anthropomorphic hand-arm systems with autonomous grasping capabilities. Both learning and control algorithms have been developed in a synergy-basedframework in order to address issues related to high dimension of the configuration space, that typically characterizes robotic hands and arms with humanlike kinematics. An experimental setup has been built to learn hand-arm motion from humans during reaching and grasping tasks. Then, a Neural Network (NN) has been realized to generalize the grasps learned by imitation. Since the NN approximates the relationship between the object characteristics and the grasp configuration of the hand-arm system, a synergy-based control strategy has been applied to overcome planning errors. The reach-to-grasp strategy has been tested on a setup constituted by the KUKA LWR 4+Arm and the SCHUNK 5-Finger Hand

Impedance control of redundant manipulators for safe human-robot collaboration

In this paper, the impedance control paradigm is used to design control algorithms for safe human-robot collaboration. In particular, the problem of controlling a redundant robot manipulator in task space, while guaranteeing a compliant behavior for the redundant degrees of freedom, is considered first. The proposed approach allows safe and dependable reaction of the robot during deliberate or accidental physical interaction with a human or the environment, thanks to null-space impedance control. Moreover, the case of control for co-manipulation is considered. In particular, the role of the kinematic redundancy and that of the impedance parameters modulation are investigated. The algorithms are verified through experiments on a 7R KUKA lightweight robot arm

The Role of Impedance Modulation and Redundancy Resolution in Human-Robot Interaction

In this work, redundancy resolution and impedance modulation strategies have been employed to enhance intuitiveness and stability in physical human-robot interaction during co-manipulation tasks. An impedance strategy to control a redundant manipulator is defined in the Cartesian space. Different modulation laws for the impedance parameters are tested in combination with different strategies to solve redundancy. The stability of the coupled human-robot system is guaranteed ensuring that the impedance parameters vary in a range evaluated experimentally. Through an extensive experimental study on a 7-DOF KUKA LWR4 arm, we show that using redundancy to decouple the equivalent inertia at the end-effector enables a more flexible choice of the impedance parameters and improves the performance during manual guidance. Moreover, variable impedance is more performant with respect to constant impedance due to a favourable compromise between accuracy and execution time and the enhanced comfort perceived by humans during manual guidance

A framework of human impedance recognition

A framework for recognizing the human intention of human forearm is developed. For a cooperative task, friendly and safe interaction is a key issue when humans directly interaction with the robots. Therefore, estimating the dynamics and intention of the human hand are very meaningful in the human machine interaction. A human subject puts his hand on the force sensor when a haptic device sets force in the proposed framework, the measured force, the surface electromyographic signal and the motion of the hand are employed to estimate the parameters of human forearm's impedance. The performance and feasibility of developed framework are verified

Synergy-based policy improvement with path integrals for anthropomorphic hands

In this work, a synergy-based reinforcement learning algorithm has been developed to confer autonomous grasping capabilities to anthropomorphic hands. In the presence of high degrees of freedom, classical machine learning techniques require a number of iterations that increases with the size of the problem, thus convergence of the solution is not ensured. The use of postural synergies determines dimensionality reduction of the search space and allows recent learning techniques, such as Policy Improvement with Path Integrals, to become easily applicable. A key point is the adoption of a suitable reward function representing the goal of the task and ensuring onestep performance evaluation. Force-closure quality of the grasp in the synergies subspace has been chosen as a cost function for performance evaluation. The experiments conducted on the SCHUNK 5-Finger Hand demonstrate the effectiveness of the algorithm showing skills comparable to human capabilities in learning new grasps and in performing a wide variety from power to high precision grasps of very small objects

Cartesian impedance control of redundant manipulators for human-robot co-manipulation

This paper addresses the problem of controlling a robot arm executing a cooperative task with a human who guides the robot through direct physical interaction. This problem is tackled by allowing the end effector to comply according to an impedance control law defined in the Cartesian space. While, in principle, the robot's dynamics can be fully compensated and any impedance behaviour can be imposed by the control, the stability of the coupled human-robot system is not guaranteed for any value of the impedance parameters. Moreover, if the robot is kinematically or functionally redundant, the redundant degrees of freedom play an important role. The idea proposed here is to use redundancy to ensure a decoupled apparent inertia at the end effector. Through an extensive experimental study on a 7-DOF KUKA LWR4 arm, we show that inertial decoupling enables a more flexible choice of the impedance parameters and improves the performance during manual guidance

Experimental Test of Synergies Computed on the SCHUNK S5FH under-actuated Hand

In this paper, a method for synergies calculation developed for an anthropomorphic 15 DOFs hand, characterized by one to one mapping between configuration space and fingertip position in the Cartesian space, has been tested on the under-actuated SCHUNK S5FH anthropomorphic hand. The grasping capabilities of the hand controlled in a three dimension synergies subspace have been tested. The results demonstrate that the data set of grasps, measured on human hands, and the mapping method of human hand synergies, based on fingertip measurements and inverse kinematics, is efficient enough to compute suitable synergies subspace where it is possible to plan and control anthropomorphic hands for grasping actions, despite on the hand kinematics and actuation system