We present an inflatable finger pad that allows regular parallel-jaw grippers to vary their grasp stiffness while maintaining a contact force and contact to non-planar surfaces. An eversible radial bellows structure made of silicone rubber allows the pad to extend to four times its original height and to retract into a rigid pod when not needed. The bellows act as passive universal joints when everted, enabling aerial contact with surfaces inclined by up to 45 degree. The bellow geometry is intentionally nonlinear but avoids bistable configurations to facilitate control. We find that the nonlinear stiffness behavior allows a pair of opposing pads to increase the compliance of a grasp twenty fold, resulting in a total of two orders of magnitude difference between the most stiff and most compliant configuration. Crucially, high compliance can be achieved while exerting a contact force between 0.7 N and 5 N, allowing for compliant but firm grasps. Pads are manufactured using printed molds and sacrificial-mold casting.

Probabilistic Movement Primitives (ProMPs) are a widely used representation of movements for human-robot interaction. They also facilitate the factorization of temporal and spatial structure of movements. In this work we investigate a method to temporally align observations so that when learning ProMPs, information in the spatial structure of the observed motion is maximized while maintaining a smooth phase velocity. We apply the method on recordings of hand trajectories in a two-dimensional reaching task. A system for simultaneous recognition of movement and phase is proposed and performance of movement recognition and movement reproduction is discussed.

Between humans, body motion is an intuitive and ubiquitous means to coordinate interactions, but so far collaborative robots have not embraced this mode of communication. One reason for this is that conventional behavior generation systems use finite state machines which make it exceptionally difficult to ingest and produce the inherently continuous and concurrent information flow that body motion provides. We propose a new, reactive motion generator based on a dynamical system instead. It mimics a conventional state machine except that transitions are not instantaneous but time-extended, reversible, and have phases. Moreover, consecutive states and transitions may overlap during execution. Most notably, more than one transition can be active at the same time if they share predecessor state. Together, these unique features enable instantaneous and gradual responses to novel information and continuous decision processes without changing the state graph itself. We demonstrate the system's capabilities in an object handover task coordinated by body motion.

In the context of robotic manipulation and grasping, the shift from a view that is static (force closure of a single posture) and contact-deprived (only contact for force closure is allowed, everything else is obstacle) towards a view that is dynamic and contact-rich (soft manipulation) has led to an increased interest in soft hands. These hands can easily exploit environmental constraints and object surfaces without risk, and safely interact with humans, but present also some challenges. Designing them is difficult, as well as predicting, modelling, and “programming” their interactions with the objects and the environment. This paper tackles the problem of simulating them in a fast and effective way, leveraging on novel and existing simulation technologies. We present a triple-layered simulation framework where dynamic properties such as stiffness are determined from slow but accurate FEM simulation data once, and then condensed into a lumped parameter model that can be used to fast simulate soft fingers and soft hands. We apply our approach to the simulation of soft pneumatic fingers.

To leverage soft hands to their full potential for grasping, we propose to design their morphology and control signals together. Considering both parameter domains makes it easier and faster to find solutions compared to fixing parameters of either domain. Additionally, the approach scales well to high-dimensional parameter spaces, which is a precondition to make automated co-design useful for soft hands. We further present an efficient simulator for simulating grasps with soft hands which is based on the SOFA framework and enables us to simulate more than a million grasps per day. These two complementary improvements promise a boost in the development of competent soft hands and their control in the future.

In many robotic applications, softness leads to improved performance, robustness, and safety, while lowering manufacturing cost, increasing versatility, and simplifying control. The advantages of soft robots derive from the fact that their behavior partially results from interactions of the robot's morphology with its environment, which is commonly referred to as morphological computation (MC). But not all MC is good in the sense that it supports the desired behavior. One of the challenges in soft robotics is to build systems that exploit the morphology (good MC) while avoiding body-environment interactions that are harmful with respect to the desired functionality (bad MC). Up to this point, constructing a competent soft robot design requires experience and intuition from the designer. This work is the first to propose a systematic approach that can be used in an automated design process. It is based on calculating a low-dimensional representation of an observed behavior, which can be used to distinguish between good and bad MC. We evaluate our method based on a set of grasping experiments, with variations in hand design, controller, and objects. Finally, we show that the information contained in the low-dimensional representation is comprehensive in the sense that it can be used to guide an automated design process.

Handshakes are common greetings, and humans therefore have strong priors of what a handshake should feel like. This makes it challenging to create compelling and realistic human-robot handshakes, necessitating the consideration of human haptic perception in the design of robot hands. At its most basic level, haptic perception is encoded by contact points and contact pressure distributions on the skin. This motivates our work on measuring the contact area and contact pressure in human handshaking interactions. We present two benchmarking experiments in this regard, measuring the contact locations in human-human/human-robot handshaking and the contact pressure distribution for handshakes with a sensorized palm. We present results from human studies with the benchmarking experiments, providing a baseline for comparison with robot hands as well as presenting new insights into human handshaking. We also show initial work in using these results for the evaluation of robot hands, and progressing towards iterative design of robot hands optimized for social hand interactions.

Soft pneumatic hands offer the advantage of intrinsic mechanical compliance. We argue that to fully leverage the compliance available in soft pneumatic actuators, they should be controlled using air mass rather than position or force, as is customary in most research in soft robotics. We propose an air-mass controller that can servo to a preset position and also allows for the exploitation of fast, mechanical compliance without additional control burden. The proposed mass control scheme is based on discrete commodity valves and pressure sensors, filling a gap in available mass control systems for small-scale soft continuum actuators. The proposed mass controller exhibits low drift for mass trajectories lasting tens of seconds, without requiring a precise model of the actuator. Continuous mass control enables applications for soft robotics, in which leveraging compliance during actuation is of central importance.

The ability to selectively stiffen otherwise compliant soft actuators increases their versatility and dexterity. We investigate granular jamming and layer jamming as two possible methods to achieve stiffening with PneuFlex actuators, a type of soft continuum actuator. The paper details five designs of jamming compartments that can be attached to an actuator. We evaluate the stiffening of the five different prototypes, achieving an up to 8-fold increase in stiffness. The strength of the most effective prototype based on layer jamming is also validated in the context of pushing buttons, resulting in an 2.23-fold increase in pushing force.

The RBO Hand is a novel, highly compliant robotic hand. It exhibits robust grasping performance, is easy to build and prototype, and very cheap to produce. One of the primary design motivations is the extensive leverage of compliance to achieve robust shape matching between the hand and the grasped object. This effect results in robust grasping performance under sensing, model, and actuation uncertainty. We show the feasibility of our approach to constructing robotic hands in extensive grasping experiments on objects with varying properties, included water bottles, eye glasses, and sheets of fabric. The RBO hand is based on a novel pneumatic actuator, called PneuFlex, which exhibits desirable properties for robotic fingers.