Grasp compliance regulation in synergistically controlled robotic hands with VSA

In this paper, we propose a general method to achieve a desired grasp compliance acting both on the joint stiffness values and on the hand configuration, also in the presence of restrictions caused by synergistic underactuation. The approach is based on the iterative exploration of the equilibrium manifold of the system and the quasi-static analysis of the governing equations. As a result, the method can cope with large commanded variations of the grasp stiffness with respect to an initial configuration. Two numerical examples are illustrated. In the first one, a simple 2D hand is analyzed so that the obtained results can be easily verified and discussed. In the second one, to show the method at work in a more realistic scenario, we model grasp compliance regulation for a DLR/HIT hand II grasping a ball

Grasp planning with soft hands using Bounding Box object decomposition

In this paper, we present a method to plan grasps for soft hands. Considering that soft hands can easily conform to the shape an the object, with preference to certain types of basic geometries and dimensions, we decompose the object into one type of these geometries, particularly into Minimal Volume Bounding Boxes (MVBBs), which are proved to be efficiently graspable by the hand we use. A set of hand poses are then generated using geometric information extracted from such MVBBs. All hand postures are used in a dynamic simulator of the PISA/IIT Soft Hand and put on a test to evaluate if a proposed hand posture leads to a successful grasp. We show, through a set of numerical simulations, that the probability of success of the hand poses generated with the proposed algorithm is very good and represents an evident improvement with respect to our previous results published in [1]

A Spherical Active Joint for Humanoids and Humans

Both humanoid robotics and prosthetics rely on the possibility of implementing spherical active joints to build dexterous robots and useful prostheses. There are three possible kinematic implementations of spherical joints: serial, parallel, and hybrid, each one with its own advantages and disadvantages. In this letter, we propose a hybrid active spherical joint, that combines the advantages of parallel and serial kinematics, to try and replicate some of the features of biological articulations: large workspace, compact size, dynamical behavior, and an overall spherical shape. We compare the workspace of the proposed joint to that of human joints, showing the possibility of an almost-complete coverage by the device workspace, which is limited only by kinematic singularities. A first prototype is developed and preliminarly tested as part of a robotic shoulder joint

Effect of Homogenous Object Stiffness on Tri-digit Grasp Properties

This paper presents experimental findings on how humans modulate their muscle activity while grasping objects of varying levels of compliance. We hypothesize that one of the key abilities that allows humans to successfully cope with uncertainties while grasping compliant objects is the ability to modulate muscle activity to control both grasp force and stiffness in a way that is coherent with the task. To that end, subjects were recruited to perform a grasp and lift task with a tripod-grasp device with contact surfaces of variable compliance. Subjects performed the task under four different compliance conditions while surface EMG from the main finger flexor and extensor muscles was recorded along with force and torque data at the contact points. Significant increases in the extensor muscle (the antagonist in the task) and co-contraction levels were found with increasing compliance at the contact points. These results suggest that the motor system may employ a strategy of increasing co-contraction, and thereby stiffness, to counteract the decreased stability in grasping compliant objects. Future experiments will examine the extent to which this phenomenon is also related to specific task features, such as precision versus power grasp and object weight

A robust iterative learning control for continuous-time nonlinear systems with disturbances

In this paper, we study the trajectory tracking problem using iterative learning control for continuous-time nonlinear systems with a generic fixed relative degree in the presence of disturbances. This class of controllers iteratively refine the control input relying on the tracking error of the previous trials and some properly tuned learning gains. Sufficient conditions on these gains guarantee the monotonic convergence of the iterative process. However, the choice of the gains is heuristically hand-tuned given an approximated system model and no information on the disturbances. Thus, in the cases of inaccurate knowledge of the model or iteration-varying measurement errors, external disturbances, and delays, the convergence condition is unlikely to be verified at every iteration. To overcome this issue, we propose a robust convergence condition, which ensures the applicability of the pure feedforward control even if other classical conditions are not fulfilled for some trials due to the presence of disturbances. Furthermore, we quantify the upper bound of the nonrepetitive disturbance that the iterative algorithm is able to handle. Finally, we validate the convergence condition simulating the dynamics of a two degrees of freedom underactuated arm with elastic joints, where one is active, and the other is passive, and a Franka Emika Panda manipulator

Modeling Human Motor Skills to Enhance Robots’ Physical Interaction

The need for users’ safety and technology acceptability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions and interactions. In this manuscript, we present a research approach that moves from the understanding of human movements and derives usefull guidelines for the planning of arm movements and the learning of skills for physical interaction of robots with the surrounding environment

A technical framework for human-like motion generation with autonomous anthropomorphic redundant manipulators

The need for users' safety and technology accept-ability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions. Classic solutions for anthropomorphic movement generation usually rely on optimization procedures, which build upon hypotheses devised from neuroscientific literature, or capitalize on learning methods. However, these approaches come with limitations, e.g. limited motion variability or the need for high dimensional datasets. In this work, we present a technique to directly embed human upper limb principal motion modes computed through functional analysis in the robot trajectory optimization. We report on the implementation with manipulators with redundant anthropomorphic kinematic architectures - although dissimilar with respect to the human model used for functional mode extraction - via Cartesian impedance control. In our experiments, we show how human trajectories mapped onto a robotic manipulator still exhibit the main characteristics of human-likeness, e.g. low jerk values. We discuss the results with respect to the state of the art, and their implications for advanced human-robot interaction in industrial co-botics and for human assistance