Enhancing Robot-Environment Physical Interaction via Optimal Impedance Profiles

Physical interaction of robots with their environment is a challenging problem because of the exchanged forces. Hybrid position/force control schemes often exhibit problems during the contact phase, whereas impedance control appears to be more simple and reliable, especially when impedance is shaped to be energetically passive. Even if recent technologies enable shaping the impedance of a robot, how best to plan impedance parameters for task execution remains an open question. In this paper we present an optimization-based approach to plan not only the robot motion but also its desired end-effector mechanical impedance. We show how our methodology is able to take into account the transition from free motion to a contact condition, typical of physical interaction tasks. Results are presented for planar and three-dimensional open-chain manipulator arms. The compositionality of mechanical impedance is exploited to deal with kinematic redundancy and multi-arm manipulation

Learning With Few Examples the Semantic Description of Novel Human-Inspired Grasp Strategies From RGB Data

Data-driven approaches and human inspiration are fundamental to endow robotic manipulators with advanced autonomous grasping capabilities. However, to capitalize upon these two pillars, several aspects need to be considered, which include the number of human examples used for training; the need for having in advance all the required information for classification (hardly feasible in unstructured environments); the trade-off between the task performance and the processing cost. In this letter, we propose a RGB-based pipeline that can identify the object to be grasped and guide the actual execution of the grasping primitive selected through a combination of Convolutional and Gated Graph Neural Networks. We consider a set of human-inspired grasp strategies, which are afforded by the geometrical properties of the objects and identified from a human grasping taxonomy, and propose to learn new grasping skills with only a few examples. We test our framework with a manipulator endowed with an under-actuated soft robotic hand. Even though we use only 2D information to reduce the footprint of the network, we achieve 90% of successful identifications of the most appropriate human-inspired grasping strategy over ten different classes, of which three were few-shot learned, outperforming an ideal model trained with all the classes, in sample-scarce conditions

Kineto-dynamic modeling of human upper limb for robotic manipulators and assistive applications

The sensory-motor architecture of human upper limb and hand is characterized by a complex inter-relation of multiple elements, such as ligaments, muscles, and joints. Nonetheless, humans are able to generate coordinated and meaningful motor actions to interact-and eventually explore-the external environment. Such a complexity reduction is usually studied within the framework of synergistic control, whose focus has been mostly limited on human grasping and manipulation. Little attention has been devoted to the spatio-temporal characterization of human upper limb kinematic strategies and how the purposeful exploitation of the environmental constraints shapes human execution of manipulative actions. In this chapter, we report results on the evidence of a synergistic control of human upper limb and during manipulation with the environment. We propose functional analysis to characterize main spatio-temporal coordinated patterns of arm joints. Furthermore, we study how the environment influences human grasping synergies. The effect of cutaneous impairment is also evaluated. Applications to the design and control of robotic and assistive devices are finally discussed

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

Association between serum Mg2+ concentrations and cardiovascular organ damage in a cohort of adult subjects

Magnesium (Mg2+) levels are associated with insulin resistance, hypertension, atherosclerosis, and type 2 diabetes (T2DM). We evaluated the clinical utility of physiological Mg2+ in assessing subclinical cardiovascular organ damage including increased carotid artery intima-media thickness (c-IMT) and left ventricular mass index (LVMI) in a cohort of well-characterized adult non-diabetic individuals. Age-and gender-adjusted correlations between Mg2+ and metabolic parameters showed that Mg2+ circulating levels were correlated negatively with body mass index (BMI), fasting glucose, and 2h-oral glucose tolerance test (OGTT) glucose. Similarly, Mg2+ levels were significantly and negatively related to c-IMT and LVMI. A multivariate regression analysis revealed that age (β = 0.440; p < 0.0001), BMI (β = 0.225; p < 0.0001), and Mg2+ concentration (β = −0.122; p < 0.01) were independently associated with c-IMT. Age (β = 0.244; p = 0.012), Mg2+ (β = −0.177; p = 0.019), and diastolic blood pressure (β = 0.184; p = 0.038) were significantly associated with LVMI in women, while age (β = 0.211; p = 0.019), Mg2+ (β = −0.171; p = 0.038) and the homeostasis model assessment index of insulin resistance (HOMA-IR) (β = −0.211; p = 0.041) were the sole variables associated with LVMI in men. In conclusion, our data support the hypothesis that the assessment of Mg2+ as part of the initial work-up might help unravel the presence of subclinical organ damage in subjects at increased risk of cardiovascular complications

A novel mechatronic system for evaluating elbow muscular spasticity relying on Tonic Stretch Reflex Threshold estimation

Muscular spasticity represents one of the most common motor disorder associated to lesions of the Central Nervous System, such as Stroke, and affects joint mobility up to the complete prevention of skeletal muscle voluntary control. Its clinical evaluation is hence of fundamental relevance for an effective rehabilitation of the affected subjects. Standard assessment protocols are usually manually performed by humans, and hence their reliability strongly depends on the capabilities of the clinical operator performing the procedures. To overcome this limitation, one solution is the usage of mechatronic devices based on the estimation of the Tonic Stretch Reflex Threshold, which allows for a quite reliable and operator-independent evaluation. In this work, we present the design and characterization of a novel mechatronic device that targets the estimation of the Tonic Stretch Reflex Threshold at the elbow level, and, at the same time, it can potentially act as a rehabilitative system. Our device can deliver controllable torque/velocity stimulation and record functional parameters of the musculo-skeletal system (joint position, torque, and multi-channel ElectroMyoGraphyc patterns), with the ultimate goals of: i) providing significant information for the diagnosis and the classification of muscular spasticity, ii) enhancing the recovery evaluation of patients undergoing through therapeutic rehabilitation procedures and iii) enabling a future active usage of this device also as therapeutic tool.Clinical relevance - The contribution presented in this work proposes a technological advancement for a device-based evaluation of motion impairment related to spasticity, with a major potential impact on both the clinical appraisal and the rehabilitation procedures

A Synergistic Behavior Underpins Human Hand Grasping Force Control During Environmental Constraint Exploitation

Despite the complex nature of human hands, neuroscientific studies suggested a simplified kinematic control underpinning motion generation, resulting in principal joint angle co-variation patterns, usually called postural hand synergies. Such a low dimensional description was observed in common grasping tasks, and was proven to be preserved also for grasps performed by exploiting the external environment (e.g., picking up a key by sliding it on a table). In this paper, we extend this analysis to the force domain. To do so, we performed experiments with six subjects, who were asked to grasp objects from a flat surface while force/torque measures were acquired at fingertip level through wearable sensors. The set of objects was chosen so that participants were forced to interact with the table to achieve a successful grasp. Principal component analysis was applied to force measurements to investigate the existence of co-variation schemes, i.e. a synergistic behavior. Results show that one principal component explains most of the hand force distribution. Applications to clinical assessment and robotic sensing are finally discussed

Design of an under-actuated wrist based on adaptive synergies

An effective robotic wrist represents a key enabling element in robotic manipulation, especially in prosthetics. In this paper, we propose an under-actuated wrist system, which is also adaptable and allows to implement different under-actuation schemes. Our approach leverages upon the idea of soft synergies - in particular the design method of adaptive synergies - as it derives from the field of robot hand design. First we introduce the design principle and its implementation and function in a configurable test bench prototype, which can be used to demonstrate the feasibility of our idea. Furthermore, we report on results from preliminary experiments with humans, aiming to identify the most probable wrist pose during the pre-grasp phase in activities of daily living. Based on these outcomes, we calibrate our wrist prototype accordingly and demonstrate its effectiveness to accomplish grasping and manipulation tasks