Amplitude tuning is an important strategy in animals employing traveling wave patterns, enhancing their adaptability to unstructured environments. This paper proposes, for the first time, an amplitude tuning method that leverages the compliance of a soft corkscrew by twisting its ends. An amplitude tunable traveling wave robot (ATWBot) is developed, consisting of a soft corkscrew housed in a high DOF cage and driven by only two servos. The soft corkscrew and cage are monolithically 3D-printed. ATWBot achieves a wide range of active amplitude tuning with passive compliance adaptation, and can extend its morphology to a coiled configuration, enabling clamping and rolling. A comprehensive model is built for the twisted soft corkscrew geometry, proving that the robot's speed is decoupled from amplitude variations during twisting. A genetic algorithm is used to optimize the soft corkscrew for achieving the fastest speed while matching the cage geometry. Experiments demonstrate that the combination of active amplitude tuning and passive body compliance enables the robot to adapt to unstructured terrains including slits, steps, gaps, converging tunnels, slopes, and swimming.

CAFEs (Collaborative Agricultural Floating Endeffectors) is a new robot design and control approach to automating large-scale agricultural tasks. Based upon a cable driven robot architecture, by sharing the same roller-driven cable set with modular robotic arms, a fast-switching clamping mechanism allows each$CAFE$to clamp onto or release from the moving cables, enabling both independent and synchronized movement across the workspace. The methods developed to enable this system include the mechanical design, precise position control and a dynamic model for the spring-mass liked system, ensuring accurate and stable movement of the robotic arms. The system's scalability is further explored by studying the tension and sag in the cables to maintain performance as more robotic arms are deployed. Experimental and simulation results demonstrate the system's effectiveness in tasks including pick-and-place showing its potential to contribute to agricultural automation.

This study presents an innovative offset-trimmed helicoids (OTH) structure, featuring a tunable deformation center that emulates the flexibility of human fingers. This design significantly reduces the actuation force needed for larger elastic deformations, particularly when dealing with harder materials like thermoplastic polyurethane (TPU). The incorporation of two helically routed tendons within the finger enables both in- plane bending and lateral out-of-plane transitions, effectively expanding its workspace and allowing for variable curvature along its length. Compliance analysis indicates that the compliance at the fingertip can be fine-tuned by adjusting the mounting placement of the fingers. This customization enhances the gripper's adaptability to a diverse range of objects. By leveraging TPU's substantial elastic energy storage capacity, the gripper is capable of dynamically rotating objects at high speeds, achieving approximately 60° in just 15 milliseconds. The three-finger gripper, with its high dexterity across six degrees of freedom, has demonstrated the capability to successfully perform intricate tasks. One such example is the adept spinning of a rod within the gripper's grasp.

Maneuverability in soft bio-inspired underwater robots, particularly for following complex trajectories, remains an unsolved challenge. In this work, we present a control approach based on a PD controller integrated with real-time camera feedback, enabling continuous and reliable free-swimming control. The system was able to maintain precise waypoint tracking for 60 minutes and more, with a minimum turning radius of 27 cm, demonstrating the robot’s high maneuverability relative to its body size. Our contributions include the development of a robust camera-based tracking system, the tuning of a PD controller to enhance trajectory following, and the exploration of the limits of maneuverability in soft swimming robots. This work paves the way for future integration with onboard sensing systems to improve state estimation in soft swimmers and reduce reliance on external camera systems.

To exploit the compliant capabilities of soft robot arms we require controller which can exploit their physical capabilities. Teleoperation, leveraging a human in the loop, is a key step towards achieving more complex control strategies. Whilst teleoperation is widely used for rigid robots, for soft robots we require teleoperation methods where the configuration of the whole body is considered. We propose a method of using an identical ‘physical twin’, or demonstrator of the robot. This tendon robot can be back-driven, with the tendon lengths providing configuration perception, and enabling a direct map-ping of tendon lengths for the execture. We demonstrate how this teleoperation across the entire configuration of the robot enables complex interactions with exploit the envrionment, such as squeezing into gaps. We also show how this method can generalize to robots which are a larger scale that the physical twin, and how, tuneability of the stiffness properties of the physical twin simplify its use.

Insects and hummingbirds exhibit remarkable agility, including full body flip maneuvers. Achieving similar maneuvers of bio-inspired tailless flapping-wing robots (FWRs) is challenging due to the complex dynamics, inherent nonlinearities and control issues. This paper presents an nonlinear model predictive control (NMPC) algorithm to enable the 360-degree flip maneuver for the developed X-wing tailless FWR, which weighs 30.8 g and has a wingspan of 14.5 cm. We first introduce a high-fidelity model of the FWR, which incorporates the aerodynamics of the wings, dynamics of the motors and servos, body kinodynamic model, and the model of thrust and torques generation. This high-fidelity model allows for testing the FWR in simulation environments, thereby reducing the damage and cost associated with flip maneuvers in real-world experiments. Based on this high-fidelity model, we propose an NMPC controller to offline compute optimal state trajectories and corresponding control inputs, which are then used as state references and the feedforward control for the FWR during its 360-degree flip maneuvers. Next, we present an online basic feedback controller that integrates the feedforward control for the FWR’s flip control. Experimental results demonstrate the successful execution of the flip maneuvers without any mechanical modifications, highlighting the effectiveness of the proposed control strategy.

Slow-speed animals can also exhibit remarkable capabilities, as seen in snails that crawl while maintaining adhesion. Snails have inspired researchers to develop traveling wave-based robots and suction robots; however, the combination of traveling wave propulsion with suction ability remains a challenge. In this paper, we propose a snail-inspired robot that integrates a corkscrew propulsion mechanism with distributed suction cups, enabling it to crawl upside down on the ceiling. The propulsion model of the corkscrew generating the traveling wave is derived, and a temporal-spatial decomposition method is applied to validate the high efficiency of traveling wave generation. The trade-off between wave amplitude and suction cup depth is investigated to determine an optimized configuration. The results show that the robot’s speed aligns well with the propulsion model. The traveling wave ratio calculated from experiments is 0.938. The optimized configuration consists of a corkscrew with a 14 mm diameter and suction cups with a 2.5 mm depth, achieving a crawling speed of 3.02 ± 0.28 mm/s while moving upside down. The combination of the proposed smooth traveling wave generation method and distributed suction cups enables the robot to crawl upside down while carrying a 200 g load and to climb a vertical wall, like a natural snail.

Proprioceptive sensing plays a crucial role in robotics, enabling closed-loop control approaches that are essential for autonomous applications. In Soft Robotics, the development and integration of sensors is even more challenging due to the compliant nature of soft bodies. Moreover, underwater environments pose additional difficulties, as sensors require to be properly embedded and sealed into the soft body of the robot. The novelty of this work lies in benchmarking different sensing technologies on a continuum soft robot to systematically assess their suitability as effective sensing approaches, both in air and underwater environments. This work presents two proprioceptive sensors (FBG optical sensor and IMUs system) embedded in an octopus-inspired robotic arm and are then tested using our proposed experimental protocol. The results underscore the system’s ability to reliably and repeatably capture data and provide a valuable guideline for the community to adopt in order to test novel sensing modalities in soft robotics. These developments are pivotal in advancing the deployment of soft robotic systems in both above- and underwater settings, facilitating tasks ranging from infrastructure inspection to marine life studies.

Teleoperated robotic manipulators enable the collection of demonstration data, which can be used to train control policies through imitation learning. However, such methods can require significant amounts of training data to develop robust policies or adapt them to new and unseen tasks. While expert feedback can significantly enhance policy performance, providing continuous feedback can be cognitively demanding and time-consuming for experts. To address this challenge, we propose using a cable-driven teleoperation system that can provide spatial corrections with 6 degrees of freedom to the trajectories generated by a policy model. Specifically, we propose a correction method termed Decaying Relative Correction (DRC), which is based upon the spatial offset vector provided by the expert and exists temporarily, reducing the number of intervention steps required by an expert. Our results demonstrate that DRC reduces the required expert intervention rate by 30% compared to a standard absolute corrective method. Furthermore, we show that integrating DRC within an online imitation learning framework rapidly increases the success rate of manipulation tasks such as raspberry harvesting and cloth wiping.

Modular soft robot arms (MSRAs) are composed of multiple modules connected in a sequence, and they can bend at different angles in various directions. This capability allows MSRAs to perform more intricate tasks than single-module robots. However, the modular structure also induces challenges in accurate planning and control. Nonlinearity and hysteresis complicate the physical model, while the modular structure and increased DOFs further lead to cumulative errors along the sequence. To address these challenges, we propose a versatile configuration space planning and control strategy for MSRAs, named$S2C2A$(State to Configuration to Action). Our approach formulates an optimization problem,$S2C$(State to Configuration planning), which integrates various loss functions and a forward model based on biLSTM to generate configuration trajectories based on target states. A configuration controller$C2A$(Configuration to Action control) based on biLSTM is implemented to follow the planned configuration trajectories, leveraging only inaccurate internal sensing feedback. We validate our strategy using a cable-driven MSRA, demonstrating its ability to perform diverse offline tasks such as position and orientation control and obstacle avoidance. Furthermore, our strategy endows MSRA with online interaction capability with targets and obstacles. Future work focuses on addressing MSRA challenges, such as more accurate physical models.

We show how a variety of techniques from Computer Graphics can be leveraged to intuitively control the shape (configuration) of arbitrary 3D Soft Robots in VR. Our pipeline, Virtual Reality Soft Robot Inverse Kinematics (VR-Soft IK), overcomes fundamental limitations of general-purpose drag-and-drop soft robot control interfaces by leaving the 2D computer screen for 3D Virtual Reality (VR). VR-Soft IK uses a simulation based on the Finite Element Method (FEM) and a control method based on sensitivity analysis. Additionally, we show that our general control pipeline can be fused with techniques from 3D character animation to skin our simulation with a high-resolution surface mesh, pointing a way toward Mixed Reality Soft Robots. This full Skinned VR-Soft IK pipeline uses skeletal animation and GPU picking. We demonstrate the utility of our pipeline by doing real-time, open-loop control of the real-world 3D soft robotic arm Helix.

Soft robotic grippers are intrinsically delicate while grasping objects, and can rely on mechanical deformation to adapt to different shapes without explicit control. These characteristics are particularly appealing for agriculture, where items of produce from the same crop can vary significantly in shape and size, and delicate harvesting is among the first concerns for fruit quality. Various soft robotic grippers have been proposed for harvesting different produce types, however their employment in field testing has been extremely limited. In this paper we developed the first closed structure soft gripper for the harvest of blackberries. We adapted an existing gripper concept, initially testing it on a sensorised raspberry physical twin. Then, followed grower-guided protocols to pick blackberries in farm polytunnels, and to evaluate the shelf life in comparison with berries picked by professional human pickers. Our results with ten experimental varieties showed a picking success rate of 95.4% demonstrating the capability of a closed structure gripper to adapt mechanically to fruit-shape variability. Moreover, a shelf life assessment on seven measured traits reported greatly improved shelf life of between 30 and 150%, across all traits for gripper harvested blackberries. Our study demonstrates the potential of soft grippers for delicate fruit harvesting, and indicates how to increase the impact of robotics in agriculture.

Learning manipulation from demonstration is a key way for humans to teach complex tasks. However, this domain mainly focuses on kinetic teaching, and does not consider imitation of interaction forces which is essential for more contact rich tasks. We propose a framework that enables robotic imitation of contact from human demonstration using a wearable finger-tip sensor. By developing a multi-modal sensor (providing both force and contact location) and robotic collection of simple training data of different motion primitives (tapping, rotation and translation), an LSTM-based model can be used to replicate motion from tactile demonstration only. To evaluate this approach, we explore the performance on increasingly complex testing data generated by a robot, and also demonstrate the full pipeline from human demonstration via the sensor used as a wearable device. This approach of using tactile sensing as a means of inferring the required robot motion paves the way for imitation of more contact-rich tasks, and enables imitation of tasks where the demonstration and imitation is performed with different body-schema.

This study explores the potential of automating robotic laboratory readiness assessment by integrating Large Language Models (LLMs) with robotic data acquisition. It investigates the capability of LLMs to detect equipment motion and operational status using visual and auditory information. Despite the challenges LLMs face in spatial analysis, this study also investigates LLM grounding methods to ensure accurate workspace assessment. By inspecting a robotic cooking setup with camera-equipped robotic arm, LLMs can detect the motion of custom equipment via color-coded marks, and identify the operational status of kitchen appliances from a single image without any physical augmentations. Additionally, device operation perceived through the emission of loud noises can be assessed by post-processing sound recordings and analyzing loudness and sound frequency metrics presented in a visual plot form. For simple spatial tasks like saucepan positioning, LLM provides accurate assessments when grounded with a single image, while complex workspace safety assessment task requires extensive knowledge of past experiences. By reviewing status of each checklist item, the LLM can decide whether experiment needs to be halted or requires human intervention, offering a set of troubleshooting steps. These findings demonstrate feasibility of the self-assessment approach for robotic laboratory systems, paving the way for future deployments.

Soft continuum manipulators are celebrated for their versatility and physical robustness to external forces and perturbations. However, this feature comes at a cost. The many degrees of freedom and compliance pose challenges for accurate pose reconstruction, both in terms of distributed sensing and pose reconstruction algorithms. Moreover, soft arms are inherently susceptible to deformation from external forces or loads, meaning that closed-loop control is essential for robust task performance. In this article, we propose the integration of multiple Inertial Measurement Units (IMUs) of a soft robot arm, Helix, for reconstruction of pose under internal and external forces. Furthermore, we integrate this dynamic pose reconstruction for kinematic-based closed-loop control strategies. By serially integrating sensing in the body of the Helix soft manipulator, we provide the system with high-frequency pose reconstruction and demonstrate improvements in end effector position with comparison to open-loop performance.

The use of aerial vehicles for exploration and data collection has the potential to significantly aid environmental monitoring in environments which are dangerous and hard to navigate. However, within these environments navigation can often be restricted by overhangs which are challenging to navigate, particularly so with the high payloads required for environmental monitoring. We propose utilizing a tethered bicopter with horizontal propellers. This spherical pendulum like system can exploit the tether, not only as a means of powering and recovering the robot, but also to assist its motion, i.e. by swinging to increase the workspace of the robot. Using PD-based control, we demonstrate how the system can be stabilized and bang-bang control to excite the system to achieve large amplitude swinging. By combining these controllers, we show how the system can be used to navigate in a glacial-inspired scenario where there are overhangs and obstacles through which the robot must navigate.

Laboratory experiments in modern food labs are human-driven and tedious processes which can have limited throughput, reliability, repeatability or robustness. Through repeatable motions and precise control of process parameters, robotic automation can provide significant improvements to the existing experimental processes, and also improve manual assessment of the sensory data. By developing a robotic automation system which performs the make, measure, adjust and clean processes for a milk beverage made from water and powdered milk, we explore how variation in different process parameters impacts quality of the beverage in terms of the measured pH value. Using collected data we also identify optimal process parameters from robustness and time-cost standpoint. By comparing performance of the robotic system to a human we demonstrate varied performance in the pH adjustment process and 3x better precision in the pH probe cleaning. We identify that designed robotic system requires 45% more time to perform the experiment when compared to a human, yet provides significant advances in terms of repeatability and reproducibility. These findings demonstrate feasibility and benefits of the robotic automation in the food lab environments, thus paving the way for the broader implementation.

For many manipulation tasks in environments such as laboratory or a kitchen, the presence of two robot arms is important to enable collaborative tasks requiring two arms (e.g. lid removal or tool use) or to improve the efficiency of scheduling of tasks. Currently, the development of multi-arm manipulation solutions has largely focused on 6 degrees of freedom articulated robot arms. However, cartesian robots have many advantages, including their precision, reliability, efficiency, and simple path planning. By developing a cartesian platform such that the end effectors of two mirrored systems can interact freely without collisions in 5 degrees of freedom, we can leverage the advantages of cartesian robots (high precision, simple planning, and low-cost hardware) and show robot cooperation. We equip each robot with end-effectors with different skills to increase the range of tasks the robots can cooperatively complete. To exploit this robotic hardware, we have developed a task-allocation and path-planning algorithm that enables these two mirror robots to work together to solve tasks collaboratively, exploiting the different skills and workspace of the two robots. We show how this robot can be used for cooperative tasks in lab automation, including pick and place, unscrewing vial caps, liquid pouring, and weighing. These demonstrate the feasibility and capabilities of the proposed robotic system for cooperative automation using cartesian robots.

Soft robotics requires effective tools to educate the next generation of engineers and researchers. Stemming from a lack of universally accepted principles for education and with high barriers to entry in terms of fabrication and hardware, education to date has been highly ad hoc. We present a low-cost toolkit based on re-configurable balloon which allows rapid development of soft yet functional robots. This provides practical demonstrations of key soft robotic principles including: morphology, stiffness control, controller dependencies and modulation of environmental interactions, while grounding robot behaviours in fundamental mechani-cal models. We provide a framework for assembling balloon structures, incorporating actuation and exploring interactions. A diverse set of robots have been developed to show the potential to use this balloon-bots for educational activities for undergraduate teaching or below. In particular, different modes of locomotion are shown using robots each of which has an assembly time under 5 minutes. These robots can teach skills ranging from component integration and implementation, to key soft robotic design principles and embodied intelligence.

Soft robots can be incredibly robust and safe but typically fail to match the strength and precision of rigid robots. This dichotomy between soft and rigid is recently starting to break down, with emerging research interest in hybrid soft-rigid robots. In this work, we draw inspiration from Nature, which achieves the best of both worlds by coupling soft and rigid tissues-like muscle and bone-to produce biological systems capable of both robustness and strength. We present foundational, general-purpose pipelines to simulate and fabricate cable-driven soft-rigid robots with embedded skeletons. We show that robots built using these methods can fluidly mimic biological systems while achieving greater force output and external load resistance than purely soft robots. Finally, we show how our simulation and fabrication pipelines can be leveraged to create more complex robots and do model-based control.

Humans have an incredible sense of self-preservation that is both instilled, and also learned through experience. One system which contributes to this is the pain and reflex system which both minimizes damage through involuntary reflex actions and also serves as a means of 'negative reinforcement’ to allow learning of poor actions or decision. Equipping robots with a reflex system and parallel learning architecture could help to prolong their useful life and allow for continued learning of safe actions. Focusing on a specific mock-up scenario of cubes on a 'stove’ like setup, we investigate the hardware and learning approaches for a robotic manipulator to learn the presence of 'hot’ objects and its contextual relationship to the environment. By creating a reflex arc using analog electronics that bypasses the 'brain’ of the system we show an increase in the speed of release by at least two-fold. In parallel we have a learning procedure which combines visual information of the scene with this 'pain signal’ to learn and predict when an object may be hot, utilizing an object detection neural network. Finally, we are able to extract the learned contextual information of the environment by introducing a method inspired by 'thought experiments' to generate heatmaps that indicate the probability of the environment being hot.

Unmanned aerial vehicles (UAVs) have broad applications including disaster response, transportation, photography, and mapping. A significant bottleneck in the development of UAVs is the limited availability of automatic tools for task-specific co-design of a UAV's shape and controller. The development of such tools is particularly challenging as UAVs can take many forms, including fixed-wing planes, radial copters, and hybrid topologies, with each class of topology showing different advantages. In this work, we present a computational design pipeline for UAVs based on a graph grammar that can search across a wide range of topologies. Graphs generated by the grammar encode different topologies and component selections, while continuous parameters encode the dimensions and properties of each component. We further augment the shape representation with deformation cages, which allow expressing a variety of wing shapes. Each UAV design is associated with an LQR controller with tunable continuous parameters. To search over this complex discrete and continuous design space, we develop a hybrid algorithm that combines discrete graph search strategies and gradient-based continuous optimization methods using a differentiable UAV simulator. We evaluate our pipeline on a set of simulated flight tasks requiring dynamic motions, showing that it discovers novel UAV designs that outperform canonical UAVs typically made by engineers.

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.

We had the great pleasure of organising the first virtual workshop on Embodied Intelligence, held on March 24–26, 2021. After the long struggle of more than a year with the pandemic, all of us were in strong need of interdisciplinary cross-fertilization events, even in a severely limited virtual setting. Even though it was a difficult time to organise anything, we had the luck of attracting over 1,000 registered participants to this event, with more than 100 presentations along with many active debates and discussions. Some of these lectures and debates are available at https://embodied-intelligence.org/.Because of the very successful event, we decided to organise this Special Issue on Embodied Intelligence in the Artificial Life journal to capture some of the discussions and document them in the format of journal publications. For this reason, the authors and reviewers of this special issue were mostly participants of the workshop. We are excited to deliver this issue to reflect the progress and challenges in this research field. The articles included in this special issue are as follows.“Machines that Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence” by George Deane discusses the roles of feelings, emotions, and moods for understanding biological intelligence and achieving artificial general intelligence. With ongoing research on active inference and self-modelling, the article argues that research in “affective feelings” plays increasingly essential roles to obtain a better understanding of computational phenomenology.“The Enactive and Interactive Dimensions of AI: Ingenuity and Imagination Through the Lens of Art and Music” by Maki Sato and Jonathan McKinney discusses the contributions of embodied and enactive approaches to AI, with a detailed analysis of an aspect of Japanese philosophy in terms of interactivity and contingent dimensions.“Evolving Modularity in Soft Robots Through an Embodied and Self-Organizing Neural Controller” by Federico Pigozzi and Eric Medvet presents research achievements in evolved soft robots. The roles of morphologies and the distributed nature of control architecture were analyzed with respect to the evolution of modularity in various simulated agents.“Braitenberg Vehicles as Developmental Neurosimulation” by Stefan Dvoretskii et al. presents recent progress in research in the developmental approach applied to the neural network of Braitenberg vehicles. Implementation of the basic principles from developmental sciences was shown to lead to the emergence of simple cognitive processes such as feedback, spatial perception, and collective behaviours.“An Embodied Intelligence-Based Biologically Inspired Strategy for Searching a Moving Target” by Julian K. P. Tan et al. reported recent analysis on search behaviours of simulated agents inspired by E. coli. The effect of embodiment was investigated to explain how simple biological systems can take advantage of it for survival.The vast field of embodied intelligence research cannot be easily covered in a single special issue, but these articles nicely bring the spirit of this research field to the Artificial Life journal. In particular, the interdisciplinary nature of Embodied Intelligence research, from basic technical research on soft robotics and mobile robots to cognitive science and philosophy, was the real fertile basis of innovative fundamental research. We hope that readers enjoy the excitement of the progress reported in this field and join the discussions in future activities of the embodied intelligence researcher community.

The sense of taste is fundamental to a human chef’s ability to cook tasty food. To develop robots that can demonstrate human-like cooking, robots need to be equipped with a sense of taste and enabled to use this perception capability to improve or understand the food which they are cooking. We propose a first study of using a salinity sensor to provide a robot with a sense of saltiness. We then demonstrate how this artificial taste receptor can be used to create an autonomous closed-loop cooking system that uses a measurement of saltiness to improve the cooking process of preparing scrambled eggs. Specifically, we show that the sensor measurements can be mapped to different taste metrics such as the overall saltiness and state of mixing the eggs, and how the cooking process can be adapted to match a human-cooked dish, hence individual preferences.

The goal of achieving `universal grasping' where many objects can be handled with minimal control input is the focus of much research due to potential high impact applications ranging from grocery packing to recycling. However, many of the grippers developed suffer from limited sensing capabilities which can prevent handing of both heavy bulky items and also lightweight delicate objects which require fine control when grasping. Sensorizing such grippers is often challenging due to the highly deformable surfaces. We propose a novel sensing approach which uses highly flexible latex bladders. By measuring changes in the air pressure of the bladders, normal force and longitudinal strain can be measured. These sensors have been integrated into a `Magic Ball' origami gripper to provide both tactile and proprioceptive sensing. The sensors show reasonable sensitivity and repeatability, are durable and low-cost, and can be easily integrated into the gripper without affecting performance. When the sensors are used for classification, they enabled identification of 10 objects with over 90% accuracy, and also allow failure to be detected through slippage detection. A control algorithm has been developed which uses the sensor feedback to extend the capabilities of the gripper to include both delicate and strong grasping. It is shown that this closed loop controller enables delicate grasping of potato chips; 80% of those tested were grasped without damage.

Abstract 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.

Prefabrication of structures is currently used in a limited capacity, due to the lack of flexibility, despite the potential cost and speed advantages. Autonomous flexible reassembly enables structures to be developed which can be continuously and iteratively dis-assembled and re-assembled providing far more flexibility in comparison to single shot pre-fabrication methods. Dis-assembly of structures should be considered when assembling, due to the asymmetry of assembly and dis-assembly processes, to ensure structures can be recycled and re-assembled. This allows for agile development, significantly reducing the time and resource usage during the build process. In this work, a framework for flexible re-assembly is developed and a robotic platform is developed to implement and test this framework with simple Lego bricks. The tradeoffs in terms of time, resource use and probability of success of this new assembly method can be understood by using a cost function to compare to alternative fabrication methods.

There is an increasing interest in the use of soft technologies for robotic application, however, the lack of advanced sensory motor capabilities is currently a significant limitation. Sensing of soft robots, in particular, is still not fully understood and methods by which a large deformable continuum body can be effectively sensed without loosing the intrinsic soft body dynamics are limited. This paper proposes a novel soft body sensing method, localized differential sensing, whereby the sensing of localized deformation on a large soft structure can be achieved with a pair of strain sensors. The design principles for this method are given. To demonstrate this sensing method Conductive Thermoplastic Elastomer (CTPE) is used for the sensing of deformation of soft body structures. A feasibility study of the approach is also presented, with strain sensors incorporated into the universal gripper to allow detection of grasped objects.

Physical computing, and robotics in particular, is an excellent tool which can be used to help students understand many aspects of Engineering and Computer Science. However, obtaining and using suitable kits or hardware can involve significant financial outlay for a school. Additionally maintaining, storing and using the physical hardware presents many practical problems for teachers. These factors combine to create a significant entry barrier, especially for teachers who have previously not used robots and may be lacking confidence or motivation to use physical computing as a teaching aid. Robotic simulation provides a good alternative as it provides the experience and many of the benefits of working with robots to be gained at low cost, using computers already available in school. A software platform which meets this need is CoSpace Rescue. This has been created by Singapore Polytechnic as an educational tool and provides a simulation platform for RoboCupJunior. This platform allows students to experience event driven programming by developing search strategies to find and deposit coloured objects, whilst avoiding obstacles and traps. CoSpace was introduced to the UK in 2013 coincident with the introduction of a new compulsory national computing curriculum for England. The approach used to introduce CoSpace into schools and the relevance that CoSpace has to the new curriculum for England are presented in this paper. Four teams have now represented the UK at international RoboCupJunior competitions and the UK national competition, now in its third year, is already attracting over 100 competing teams with over 300 schools making use of the CoSpace Platform nationwide.