In the recent years, a clear trend towards simplification emerged in the development of robotic hands. The use of soft robotic approaches has been a useful tool in this prospective, enabling complexity reduction by embodying part of grasping intelligence in the hand mechanical structure. Several hand prototypes designed according to such principles have accomplished good results in terms of grasping simplicity, robustness, and reliability. Among them, the Pisa/IIT SoftHand demonstrated the feasibility of a large variety of grasping tasks, by means of only one actuator and an opportunely designed tendon driven differential mechanism. However, the use of a single degree of actuation prevents the execution of more complex tasks, like fine pre-shaping of fingers and in-hand manipulation. While possible in theory, simply doubling the Pisa/IIT SoftHand actuation system has several disadvantages, e.g. in terms of space and mechanical complexity. To overcome these limitations we propose a novel design framework for tendon driven mechanisms, where the main idea is to turn transmission friction from a disturbance into a design tool. In this way the degrees of actuation can be doubled with little additional complexity.
By leveraging on this idea we design a novel robotic hand, the Pisa/IIT SoftHand 2. We present here its design, modeling, control, and experimental validation. The hand demonstrates that by opportunely combining only two degrees of actuation with hand softness, a large variety of grasping and manipulation tasks can be performed only relying on the intelligence embodied in the mechanism. Examples include rotating objects with different shapes, opening a jar, pouring coffee from a glass.
Efficient Walking Gait Generation via Principal Component Representation of Optimal Trajectories: Application to a Planar Biped Robot With Elastic Joints
Recently, the method of choice to exploit robot dynamics for efficient walking is numerical optimization (NO). The main drawback in NO is the computational complexity, which strongly affects the time demand of the solution. Several strategies can be used to make the optimization more treatable and to efficiently describe the solution set. In this letter, we present an algorithm to encode effective walking references, generated offline via numerical optimization, extracting a limited number of principal components and using them as a basis of optimal motions. By combining these components, a good approximation of the optimal gaits can be generated at run time. The advantages of the presented approach are discussed, and an extensive experimental validation is carried out on a planar legged robot with elastic joints. The biped thus controlled is able to start and stop walking on a treadmill, and to control its speed dynamically as the treadmill speed changes.
The rich variety of human upper limb movements requires an extraordinary coordination of different joints according to specific spatio-temporal patterns. However, unvealing these motor schemes is a challenging task. Principal components have been often used for analogous purposes, but such an approach relies on hypothesis of temporal uncorrelation of upper limb poses in time. To overcome these limitations, in this work, we leverage on functional principal component analysis (fPCA). We carried out experiments with 7 subjects performing a set of most significant human actions, selected considering state-of-the-art grasp taxonomies and human kinematic workspace. fPCA results show that human upper limb trajectories can be reconstructed by a linear combination of few principal time-dependent functions, with a first component alone explaining around 60/70% of the observed behaviors. This allows to infer that in daily living activities humans reduce the complexity of movement by modulating their motions through a reduced set of few principal patterns. Finally, we discuss how this approach could be profitably applied in robotics and bioengineering, opening fascinating perspectives to advance the state of the art of artificial systems, as it was the case of hand synergies.
Metabolic disorders due to over-nutrition are a major global health problem, often associated with obesity and related morbidities. Obesity is peculiar to humans, as it is associated with lifestyle and diet, and so difficult to reproduce in animal models. Here we describe a model of human central adiposity based on a 3-tissue system consisting of a series of interconnected fluidic modules. Given the causal link between obesity and systemic inflammation, we focused primarily on pro-inflammatory markers, examining the similarities and differences between the 3-tissue model and evidence from human studies in the literature. When challenged with high levels of adiposity, the in-vitro system manifests cardiovascular stress through expression of E-selectin and von Willebrand factor as well as systemic inflammation (expressing IL-6 and MCP-1) as observed in humans. Interestingly, most of the responses are dependent on the synergic interaction between adiposity and the presence of multiple tissue types. The set-up has the potential to reduce animal experiments in obesity research and may help unravel specific cellular mechanisms which underlie tissue response to nutritional overload.
Current prosthetic hands are frequently rejected in part due to limited functionality and versatility. We assessed the feasibility of a novel prosthetic hand, the SoftHand Pro (SHP), whose design combines soft robotics and hand postural synergies. Able-bodied subjects (n = 23) tracked cursor motion by opening and closing the SHP and performed a grasp-lift-hold-release (GLHR) task with a sensorized cylindrical object of variable weight. The SHP control was driven by electromyographic (EMG) signals from two antagonistic muscles. Although the time to perform the GLHR task was longer for the SHP than native hand for the first few trials (10.2 ± 1.4 s and 2.13 ± 0.09 s, respectively), performance was much faster on subsequent trials (~5 s). The SHP steady-state grip force was significantly modulated as a function of object weight (p <; 0.001). For the native hand, however, peak and steady-state grip forces were modulated to a greater extent (+68% and +91%, respectively). These changes were mediated by the modulation of EMG amplitude and co-contraction. These data suggest that the SHP has a promise for prosthetic applications and point-to-design modifications that could improve the SHP.
Research reported in this publication was supported by the Grainger Foundation, the Eunice Kennedy Shriver National Institute Of Child Health and Human Development of the National Institutes of Health (NIH) under Award Number R21HD081938, and the European Commission within Horizon 2020 2015.ICT.24a Robot- ics RIA, with Grant no. 688857 “SOFTPRO -Synergy-based Open-source Foundations and Technologies for Prosthetics and RehabilitatiOn.”
This work was supported by the European Commission project (Horizon 2020 research program) SOFTPRO 688857, the European Research Council under the Advanced Grant SoftHands “A Theory of Soft Synergies for a New Generation of Artificial Hands,” ERC-291166, and the Proof of Concept Project SoftHand Pro-H, ERC-2016-PoC 727536.
Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects
