AN UNDERACTUATED MECHANICAL HAND PROSTHESYS BY IFToMM ITALY

This paper describes a mechanical underactuated hand, whose design is under patenting. The proposed hand can be used as robot grasping end-effector and, mainly, as a human prosthesis. The proposed underactuated mechanism is based on an adaptive scheme, hence it permits to move five fingers with only one actuator. The actuator is connected to a set of pulleys that operate five tendons. Each tendon will move the phalanxes of a finger. The proposed mechanism permits each finger to adapt its configuration to almost any object shape so that each of the fingers will grasp the object independently on the configuration of the finger itself and independently on the configuration of the other fingers. The tendons are un-extendible so that each finger will grasp an object always with the same force, regardless of object shape. The overall grasping force will be controlled just by adjusting the input actuator torque. This paper also reports preliminary kinematic and dynamic studies aiming to a validation of the feasibility of the proposed design solution. Finally an early experimental prototype is shown

A synergy-driven approach to a myoelectric hand

In this paper, we present the Pisa/IIT SoftHand with myoelectric control as a synergy-driven approach for a prosthetic hand. Commercially available myoelectric hands are more expensive, heavier, and less robust than their bodypowered counterparts; however, they can offer greater freedom of motion and a more aesthetically pleasing appearance. The Pisa/IIT SoftHand is built on the motor control principle of synergies through which the immense complexity of the hand is simplified into distinct motor patterns. As the SoftHand grasps, it follows a synergistic path with built-in flexibility to allow grasping of a wide variety of objects with a single motor. Here we test, as a proof-of-concept, 4 myoelectric controllers: a standard controller in which the EMG signal is used only as a position reference, an impedance controller that determines both position and stiffness references from the EMG input, a standard controller with vibrotactile force feedback, and finally a combined vibrotactile-impedance (VI) controller. Four healthy subjects tested the control algorithms by grasping various objects. All controllers were sufficient for basic grasping, however the impedance and vibrotactile controllers reduced the physical and cognitive load on the user, while the combined VI mode was the easiest to use of the four. While these results need to be validated with amputees, they suggest a low-cost, robust hand employing hardware-based synergies is a viable alternative to traditional myoelectric prostheses

Autonomous Object Handover Using Wrist Tactile Information

Grasping in an uncertain environment is a topic of great interest in robotics. In this paper we focus on the challenge of object handover capable of coping with a wide range of different and unspecified objects. Handover is the action of object passing an object from one agent to another. In this work handover is performed from human to robot. We present a robust method that relies only on the force information from the wrist and does not use any vision and tactile information from the fingers. By analyzing readings from a wrist force sensor, models of tactile response for receiving and releasing an object were identified and tested during validation experiments

Detecting Object Affordances with Convolutional Neural Networks

We present a novel and real-time method to detect object affordances from RGB-D images. Our method trains a deep Convolutional Neural Network (CNN) to learn deep features from the input data in an end-to-end manner. The CNN has an encoder-decoder architecture in order to obtain smooth label predictions. The input data are represented as multiple modalities to let the network learn the features more effectively. Our method sets a new benchmark on detecting object affordances, improving the accuracy by 20% in comparison with the state-of-the-art methods that use hand-designed geometric features. Furthermore, we apply our detection method on a full-size humanoid robot (WALK-MAN) to demonstrate that the robot is able to perform grasps after efficiently detecting the object affordances

Manipulation Framework for Compliant Humanoid COMAN: Application to a Valve Turning Task

With the purpose of achieving a desired interaction performance for our compliant humanoid robot (COMAN), in this paper we propose a semi-autonomous control framework and evaluate it experimentally in a valve turning setup. The control structure consists of various modules and interfaces to identify the valve, locate the robot in front of it and perform the manipulation. The manipulation module implements four motion primitives (Reach, Grasp, Rotate and Disengage) and realizes the corresponding desired impedance profile for each phase to accomplish the task. In this direction, to establish a stable and compliant contact between the valve and the robot hands, while being able to generate the sufficient rotational torques depending on the valve's friction, Rotate incorporates a novel dual-arm impedance control technique to plan and realize a task-appropriate impedance profile. Results of the implementation of the proposed control framework are firstly evaluated in simulation studies using Gazebo. Subsequent experimental results highlight the efficiency of the proposed impedance planning and control in generation of the required interaction forces to accomplish the task

An Underactuated Multi-finger Grasping Device

In this paper, a mechanical model for an underactuated multi-finger grasping device is presented. The device has single-tendon, three-phalanx fingers, all moved by only one actuator. By means of the model, both the kinematic and dynamical behaviour of the finger itself can be studied. The finger is part of a more complex mechanical system that consists of a four-finger grasping device for robots or a five-finger human hand prosthesis. Some results of both the kinematic and dynamical behaviour are also presented

Teleimpedance Control of a Synergy-Driven Anthropomorphic Hand

In this paper, a novel synergy driven teleimpedance controller for the Pisa–IIT SoftHand is presented. Towards the development of an efficient, robust, and low-cost hand prothesis, the Pisa–IIT SoftHand is built on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. As the SoftHand grasps, it follows a synergistic path with built-in flexibility to allow grasping of objects of various shapes using only a single motor. In this work, the hand grasping motion is regulated with an impedance controller which incorporates the user’s postural and stiffness synergy profiles in realtime. In addition, a disturbance observer is realized which estimates the grasping contact force. The estimated force is then fedback to the user via a vibration motor. Grasp robustness and transparency improvements were evaluated on two healthy subjects while grasping different objects. Implementation of the proposed teleimpedance controller led to the execution of stable grasps by controlling the grasping forces, via modulation of hand compliance. In addition, utilization of the vibrotactile feedback resulted in reduced physical load on the user. While these results need to be validated with amputees, they provide evidence that a low-cost, robust hand employing hardwarebased synergies is a viable alternative to traditional myoelectric prostheses

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