Soft Robotics: A Route to Equality, Diversity, and Inclusivity in Robotics.

Robotics is entering our daily lives. The discipline is increasingly crucial in fields such as agriculture, medicine, and rescue operations, impacting our food, health, and planet. At the same time, it is becoming evident that robotic research must embrace and reflect the diversity of human society to address these broad challenges effectively. In recent years, gender inclusivity has received increasing attention, but it still remains a distant goal. In addition, awareness is rising around other dimensions of diversity, including nationality, religion, and politics. Unfortunately, despite the efforts, empirical evidence shows that the field has still a long way to go before achieving a sufficient level of equality, diversity, and inclusion across these spectra. This study focuses on the soft robotics community-a growing and relatively recent subfield-and it outlines the present state of equality and diversity panorama in this discipline. The article argues that its high interdisciplinary and accessibility make it a particularly welcoming branch of robotics. We discuss the elements that make this subdiscipline an example for the broader robotic field. At the same time, we recognize that the field should still improve in several ways and become more inclusive and diverse. We propose concrete actions that we believe will contribute to achieving this goal, and provide metrics to monitor its evolution.

Soft robotics for farm to fork: applications in agriculture & farming.

Agricultural tasks and environments range from harsh field conditions with semi-structured produce or animals, through to post-processing tasks in food-processing environments. From farm to fork, the development and application of soft robotics offers a plethora of potential uses. Robust yet compliant interactions between farm produce and machines will enable new capabilities and optimize existing processes. There is also an opportunity to explore how modeling tools used in soft robotics can be applied to improve our representation and understanding of the soft and compliant structures common in agriculture. In this review, we seek to highlight the potential for soft robotics technologies within the food system, and also the unique challenges that must be addressed when developing soft robotics systems for this problem domain. We conclude with an outlook on potential directions for meaningful and sustainable impact, and also how our outlook on both soft robotics and agriculture must evolve in order to achieve the required paradigm shift.

Non-contact robotic manipulation of floating objects: exploiting emergent limit cycles.

The study of non-contact manipulation in water, and the ability to robotically control floating objects has gained recent attention due to wide-ranging potential applications, including the analysis of plastic pollution in the oceans and the optimization of procedures in food processing plants. However, modeling floating object movements can be complex, as their trajectories are influenced by various factors such as the object's shape, size, mass, and the magnitude, frequency, and patterns of water waves. This study proposes an experimental investigation into the emergence ofrobotically controlled limit cycles in the movement of floating objects within a closed environment. The objects' movements are driven by robot fins, and the experiment plan set up involves the use of up to four fins and variable motor parameters. By combining energy quantification of the system with an open-loop pattern generation, it is possible to demonstrate all main water-object interactions within the enclosed environment. A study using dynamic time warping around floating patterns gives insights on possible further studies.

Prescribing Cartesian Stiffness of Soft Robots by Co-Optimization of Shape and Segment-Level Stiffness.

Soft robots aim to revolutionize how robotic systems interact with the environment thanks to their inherent compliance. Some of these systems are even able to modulate their physical softness. However, simply equipping a robot with softness will not generate intelligent behaviors. Indeed, most interaction tasks require careful specification of the compliance at the interaction point; some directions must be soft and others firm (e.g., while drawing, entering a hole, tracing a surface, assembling components). On the contrary, without careful planning, the preferential directions of deformation of a soft robot are not aligned with the task. With this work, we propose a strategy to prescribe variations of the physical stiffness and the robot's posture so to implement a desired Cartesian stiffness and location of the contact point. We validate the algorithm in simulation and with experiments. To perform the latter, we also present a new tendon-driven soft manipulator, equipped with variable-stiffness segments and proprioceptive sensing and capable to move in three dimensional. We show that, combining the intelligent hardware with the proposed algorithm, we can obtain the desired stiffness at the end-effector over the workspace.

Robotic automation and unsupervised cluster assisted modeling for solving the forward and reverse design problem of paper airplanes.

Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory forms and flight distances. This makes optimization and understanding of their behavior challenging for humans. By understanding the behavior of paper airplanes and predicting flight behavior, there is a potential to improve the design of aerial vehicles that operate at low Reynolds numbers. By developing a robotic system that can fabricate, test, analyze, and model the flight behavior in an unsupervised fashion, a wide design space can be reliably characterized. We find there are discrete behavioral groups that result in different trajectories: nose dive, glide, and recovery glide. Informed by this characterization we propose a method of using Gaussian mixture models to extract the clusters of the design space that map to these different behaviors. This allows us to solve both the forward and reverse design problem for paper airplanes, and also to perform efficient optimization of the geometry for a given target flight distance.

Exploration of the Design of Spiderweb-Inspired Structures for Vibration-Driven Sensing.

In the quest to develop large-area soft sensors, we can look to nature for many examples. Spiderwebs show many fascinating properties that we can seek to understand and replicate in order to develop large-area, soft, and deformable sensing structures. Spiders' webs are used not only to capture prey, but also to localize their prey through the vibrations that they feel through their legs. Inspired by spiderwebs, we developed a large-area tactile sensor for localizing contact points through vibration sensing. We hypothesize that the structure of a web can be leveraged to amplify, filter, or otherwise morphologically tune vibrations to improve sensing capabilities. To explore this design space, we created a means of computationally designing and 3D printing web structures. By using vibration sensors mounted on the edges of webs to simulate a spider monitoring vibrations, we show how varying the structural properties affects the localization performance when using vibration sensors and long short-term memory (LSTM)-based neural network classifiers. We seek to explain the classification performance seen in different webs by considering various metrics of information content for different webs and, hence, provide insight into how bio-inspired spiderwebs can be used to assist large-area sensing structures.

Climate-informed forecasts reveal dramatic local habitat shifts and population uncertainty for northern boreal caribou.

Most research on boreal populations of woodland caribou (Rangifer tarandus caribou) has been conducted in areas of high anthropogenic disturbance. However, a large portion of the species' range overlaps relatively pristine areas primarily affected by natural disturbances, such as wildfire. Climate-driven habitat change is a key concern for the conservation of boreal-dependent species, where management decisions have yet to consider knowledge from multiple ecological domains integrated into a cohesive and spatially explicit forecast of species-specific habitat and demography. We used a novel ecological forecasting framework to provide climate-sensitive projections of habitat and demography for five boreal caribou monitoring areas within the Northwest Territories (NWT), Canada, over 90 years. Importantly, we quantify uncertainty around forecasted mean values. Our results suggest habitat suitability may increase in central and southwest regions of the NWT's Taiga Plains ecozone but decrease in southern and northwestern regions driven by conversion of coniferous to deciduous forests. We do not project that boreal caribou population growth rates will change despite forecasted changes to habitat suitability. Our results emphasize the importance of efforts to protect and restore northern boreal caribou habitat despite climate uncertainty while highlighting expected spatial variations that are important considerations for local people who rely on them. An ability to reproduce previous work, and critical thought when incorporating sources of uncertainty, will be important to refine forecasts, derive management decisions, and improve conservation efficacy for northern species at risk.

Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design.

Robotic hands have long strived to reach the performance of human hands. The physical complexity and extraordinary capabilities of the human hand, in terms of sensing, actuation, and cognitive abilities, make achieving this goal challenging. At the heart of the physical structure of the hand is its' passive behaviors. Seen most clearly in soft robotic hands, these behaviors influence and affect the mechanical, sensing, and control functionalities. With this perspective, we present a framework through which passivity in robot hands can be understood, by concretely identifying the role of passivity in the design, fabrication, and control of soft hands. In this framework we focus on the interactions between the physical hand and the: environment, internal actuation, sensor morphology, and wrist control. Taking these surrounding systems away, we are left with a passive soft hand whose behaviors emerge from external interactions. Inspired by the human hand, we define the role of these four key interacting pillars and review how state-of-the art robot hands utilize these four elements to aid functionality. We show how these pillars promote hybrid soft-rigid hands with rich behaviors, providing benefits in terms of the increased adaptability to uncertain environments, improved scalability and reduction in the cost of actuation, sensing, and control. This review provides a conceptual framework for approaching hand design and analysis through consideration of the passive behaviors. This highlights not only the advances that can be made by approaching the problem in this way but also the outstanding challenges that stem from this outlook.

Scalable Fabrication and Actuation of a Human Inspired Hand Through 3D Printed Flexures and Combinatorial Actuation.

The fabrication and control of robot hands with biologically inspired structure remains challenging due to its cost and complexity. In this paper we explore how widely available FDM printers can be used to fabricate complex hand structures by leveraging compliant PLA flexures. In particular, we focus on the fabrication of fingers printed as a single piece with tunable compliance, a multi degree of freedom thumb joint, and sensorized compliant fingertips. To address the challenge of control and actuation, we model the behavior of the flexure joints and propose a new method for control: combinatorial actuation. This control method combines the use of a single continuous actuated tendon per finger with two shared "combinatorial" actuators which act across all fingers. We demonstrate that the fingertip workspace using this method is comparable to fully actuated fingers while using significantly less independent actuators. The proposed approach of fabrication and combinatorial actuation provides a rapid and scalable method of designing and controlling complex manipulators.

Action Augmentation of Tactile Perception for Soft-Body Palpation.

Medical palpation is a diagnostic technique in which physicians use the sense of touch to manipulate the soft human tissue. This can be done to enable the diagnosis of possibly life-threatening conditions, such as cancer. Palpation is still poorly understood because of the complex interaction dynamics between the practitioners' hands and the soft human body. To understand this complex of soft body interactions, we explore robotic palpation for the purpose of diagnosing the presence of abnormal inclusions, or tumors. Using a Bayesian framework for training and classification, we show that the exploration of soft bodies requires complex, multi-axis, palpation trajectories. We also find that this probabilistic approach is capable of rapidly searching the large action space of the robot. This work progresses "robotic" palpation, and it provides frameworks for understanding and exploiting soft body interactions.

The science of soft robot design: A review of motivations, methods and enabling technologies.

Novel technologies, fabrication methods, controllers and computational methods are rapidly advancing the capabilities of soft robotics. This is creating the need for design techniques and methodologies that are suited for the multi-disciplinary nature of soft robotics. These are needed to provide a formalized and scientific approach to design. In this paper, we formalize the scientific questions driving soft robotic design; what motivates the design of soft robots, and what are the fundamental challenges when designing soft robots? We review current methods and approaches to soft robot design including bio-inspired design, computational design and human-driven design, and highlight the implications that each design methods has on the resulting soft robotic systems. To conclude, we provide an analysis of emerging methods which could assist robot design, and we present a review some of the necessary technologies that may enable these approaches.

Soft Self-Healing Fluidic Tactile Sensors with Damage Detection and Localization Abilities.

Self-healing sensors have the potential to increase the lifespan of existing sensing technologies, especially in soft robotic and wearable applications. Furthermore, they could bestow additional functionality to the sensing system because of their self-healing ability. This paper presents the design for a self-healing sensor that can be used for damage detection and localization in a continuous manner. The soft sensor can recover full functionality almost instantaneously at room temperature, making the healing process fully autonomous. The working principle of the sensor is based on the measurement of air pressure inside enclosed chambers, making the fabrication and the modeling of the sensors easy. We characterize the force sensing abilities of the proposed sensor and perform damage detection and localization over a one-dimensional and two-dimensional surface using multilateration techniques. The proposed solution is highly scalable, easy-to-build, cheap and even applicable for multi-damage detection.

Morphological Sensitivity and Falling Behavior of Paper V-Shapes.

Behavioral diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context we are interested in falling paper systems, specifically the V-shaped falling paper (VSFP) system that exhibits a set of discrete falling behaviors across the morphological parameter space. Our previous work has investigated how morphology influences dominant falling behaviors in the VSFP system. In this article we build on this analysis to investigate the nature of behavioral transitions in the same system. First, we investigate stochastic behavior transitions. We demonstrate how morphology influences the likelihood of different transitions, with certain morphologies leading to a wide range of possible paths through the behavior-space. Second, we investigate deterministic transitions. To investigate behaviors over longer time periods than available in falling experiments we introduce a new experimental platform. We demonstrate how we can induce behavior transitions by modulating the energy input to the system. Certain behavior transitions are found to be irreversible, exhibiting a form of hysteresis, while others are fully reversible. Certain morphologies are shown to behave like simplistic sequential logic circuits, indicating that the system has a form of memory encoded into the morphology-environment interactions. Investigating the limits of how morphology-environment interactions induce non-trivial behaviors is a key step for the design of embodied artificial life-forms.

Online Morphological Adaptation for Tactile Sensing Augmentation.

Sensor morphology and structure has the ability to significantly aid and improve tactile sensing capabilities, through mechanisms such as improved sensitivity or morphological computation. However, different tactile tasks require different morphologies posing a challenge as to how to best design sensors, and also how to enable sensor morphology to be varied. We introduce a jamming filter which, when placed over a tactile sensor, allows the filter to be shaped and molded online, thus varying the sensor structure. We demonstrate how this is beneficial for sensory tasks analyzing how the change in sensor structure varies the information that is gained using the sensor. Moreover, we show that appropriate morphology can significantly influence discrimination, and observe how the selection of an appropriate filter can increase the object classification accuracy when using standard classifiers by up to 28%.

Wrist-driven passive grasping: interaction-based trajectory adaption with a compliant anthropomorphic hand.

The structure of the human musculo-skeletal systems shows complex passive dynamic properties, critical for adaptive grasping and motions. Through wrist and arm actuation, these passive dynamic properties can be exploited to achieve nuanced and diverse environment interactions. We have developed a passive anthropomorphic robot hand that shows complex passive dynamics. We require arm/wrist control with the ability to exploit these. Due to the soft hand structures and high degrees of freedom during passive-object interactions, bespoke generation of wrist trajectories is challenging. We propose a new approach, which takes existing wrist trajectories and adapts them to changes in the environment, through analysis and classification of the interactions. By analysing the interactions between the passive hand and object, the required wrist motions to achieve them can be mapped back to control of the hand. This allows the creation of trajectories which are parameterized by object size or task. This approach shows up to 86% improvement in grasping success rate with a passive hand for object size changes up to ±50%.

A field-tested robotic harvesting system for iceberg lettuce.

Agriculture provides an unique opportunity for the development of robotic systems; robots must be developed which can operate in harsh conditions and in highly uncertain and unknown environments. One particular challenge is performing manipulation for autonomous robotic harvesting. This paper describes recent and current work to automate the harvesting of iceberg lettuce. Unlike many other produce, iceberg is challenging to harvest as the crop is easily damaged by handling and is very hard to detect visually. A platform called Vegebot has been developed to enable the iterative development and field testing of the solution, which comprises of a vision system, custom end effector and software. To address the harvesting challenges posed by iceberg lettuce a bespoke vision and learning system has been developed which uses two integrated convolutional neural networks to achieve classification and localization. A custom end effector has been developed to allow damage free harvesting. To allow this end effector to achieve repeatable and consistent harvesting, a control method using force feedback allows detection of the ground. The system has been tested in the field, with experimental evidence gained which demonstrates the success of the vision system to localize and classify the lettuce, and the full integrated system to harvest lettuce. This study demonstrates how existing state-of-the art vision approaches can be applied to agricultural robotics, and mechanical systems can be developed which leverage the environmental constraints imposed in such environments.

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review.

We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.

Physics driven behavioural clustering of free-falling paper shapes.

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour.

Multi-Functional Soft Strain Sensors for Wearable Physiological Monitoring.

Wearable devices which monitor physiological measurements are of significant research interest for a wide number of applications including medicine, entertainment, and wellness monitoring. However, many wearable sensing systems are highly rigid and thus restrict the movement of the wearer, and are not modular or customizable for a specific application. Typically, one sensor is designed to model one physiological indicator which is not a scalable approach. This work aims to address these limitations, by developing soft sensors and including conductive particles into a silicone matrix which allows sheets of soft strain sensors to be developed rapidly using a rapid manufacturing process. By varying the morphology of the sensor sheets and electrode placement the response can be varied. To demonstrate the versatility and range of sensitivity of this base sensing material, two wearable sensors have been developed which show the detection of different physiological parameters. These include a pressure-sensitive insole sensor which can detect ground reaction forces and a strain sensor which can be worn over clothes to allow the measurements of heart rate, breathing rate, and gait.