Closed-Loop Optimization of Soft Sensor Morphology Using 3D Printing of Electrically Conductive Hydrogel

Soft sensing technologies provide a novel alternative for state estimation in wearables and robotic systems. They allow one to capture intrinsic state parameters in a highly conformable manner. However, due to the nonlinearities in the materials that make up a soft sensor, it is difficult to develop accurate models of these systems. Consequently, design of these soft sensors is largely user defined or based on trial and error. Since these sensors conform and take the shape of the sensing body, these issues are further exacerbated when they are installed. Herein, a framework for the automated design optimization of soft sensors using closed-loop 3D printing of a recyclable hydrogel-based sensing material is presented. The framework allows direct printing of the sensor on the sensing body using visual feedback, evaluates the sensor performance, and iteratively improves the sensor design. Following preliminary investigations into the material and morphology parameters, this is demonstrated through the optimization of a sensorized glove which can be matched to specific tasks and individual hand shapes. The glove's sensors are tuned to respond only to particular hand poses, including distinguishing between two similar tennis racket grip techniques

Machine Learning for Soft Robot Sensing and Control: A Tutorial Study

Developing feedback controllers for robots with embedded sensors is challenging and typically requires expert knowledge. As machine learning (ML) advances, the development of learning-based controllers has become more and more accessible, even to non-experts. This work presents the development of a tutorial to educate non-roboticists about ML-based sensing and control in cyber-physical systems using a soft robotic device. We demonstrated this by creating a recurrent neural network-based closed-loop force controller for a soft finger with embedded soft sensors. Our hypothesis is validated in a 2.5-hour workshop session for students with no prior knowledge of robot control. This work serves as a tutorial for participants aiming to experience and perform a general benchmark for soft robot control tasks, with little or even no expertise in robotics

Autonomous dishwasher loading from cluttered trays using pre-trained deep neural networks

Abstract: Autonomous dishwasher loading is a benchmark problem in robotics that highlights the challenges of robotic perception, planning, and manipulation in an unstructured environment. Current approaches resort to a specialized solution, however, these technologies are not viable in a domestic setting. Learning‐based solutions seem promising for a general purpose solutions; however, they require large amounts of catered data to be applied in real‐world scenarios. This article presents a novel learning‐based solution without a training phase using pre‐trained object detection networks. By developing a perception, planning, and manipulation framework around an off‐the‐shelf object detection network, we are able to develop robust pick‐and‐place solutions that are easy to develop and general purpose requiring only a RGB feedback and a pinch gripper. Analysis of a real‐world canteen tray data is first performed and used for developing our in‐lab experimental setup. Our results obtained from real‐world scenarios indicate that such approaches are highly desirable for plug‐and‐play domestic applications with limited calibration. All the associated data and code of this work are shared in a public repository

First-Order Dynamic Modeling and Control of Soft Robots

Modeling of soft robots is typically performed at the static level or at a second-order fully dynamic level. Controllers developed upon these models have several advantages and disadvantages. Static controllers, based on the kinematic relations tend to be the easiest to develop, but by sacrificing accuracy, efficiency and the natural dynamics. Controllers developed using second-order dynamic models tend to be computationally expensive, but allow optimal control. Here we propose that the dynamic model of a soft robot can be reduced to first-order dynamical equation owing to their high damping and low inertial properties, as typically observed in nature, with minimal loss in accuracy. This paper investigates the validity of this assumption and the advantages it provides to the modeling and control of soft robots. Our results demonstrate that this model approximation is a powerful tool for developing closed-loop task-space dynamic controllers for soft robots by simplifying the planning and sensory feedback process with minimal effects on the controller accuracy

Towards Growing Robots: A Piecewise Morphology-Controller Co-adaptation Strategy for Legged Locomotion

Control of robots has largely been based on the assumption of a fixed morphology. Accordingly, robot designs have been stationary in time, except for the case of modular robots. Any drastic change in morphology, hence, requires a remodelling of the controller. This work takes inspiration from developmental robotics to present a piecewise morphology-controller growth/adaptation strategy that facilitates fast and reliable control adaptation to growing robots. We demonstrate our methodology on a simple 3 degree of freedom walking robot with adjustable foot lengths and with varying inertial conditions. Our results show not only the effectiveness and reliability of the piecewise morphology controller co-adaptation (PMCCA) strategy, but also highlight the need for morphological adaptation as a robot design strategy

Self-healing ionic gelatin/glycerol hydrogels for strain sensing applications

Soft sensing technologies have the potential to revolutionize wearable devices, haptic interfaces and robotic systems. However, there are numerous challenges in their deployment due to their poor resilience, high-energy consumption, and omnidirectional strain responsivity. This work reports the development of a versatile ionic gelatin-glycerol hydrogel for soft sensing applications. The sensing device is cheap and easy to manufacture, self-healable at room temperature, can undergo strains of up to 454%, is stable over long periods of time, biocompatible and biodegradable. It is ideal for strain sensing applications, with R^2 = 0.9971 linearity and a pressure insensitive conduction mechanism. Experimental results show the applicability of the ionic hydrogel for wearable devices and soft robotic technologies for strain, humidity and temperature sensing, while being able to partially self-heal at room temperatures.SHERO project, a Future and Emerging Technologies (FET) programme of the European Commission (grant agreement ID 828818). EPSRC DTP (EP/R513180/1)

Improving Robotic Cooking using Batch Bayesian Optimization

With advances in the field of robotic manipulation, sensing and machine learning, robotic chefs are expected to become prevalent in our kitchens and restaurants. Robotic chefs are envisioned to replicate human skills in order to reduce the burden of the cooking process. However, the potential of robots as a means to enhance the dining experience is unrecognised. This work introduces the concept of food quality optimization and its challenges with an automated omelette cooking robotic system. The design and control of the robotic system that uses general kitchen tools is presented first. Next, we investigate new optimization strategies for improving subjective food quality rating, a problem challenging because of the qualitative nature of the objective and strongly constrained number of function evaluations possible. Our results show that through appropriate design of the optimization routine using Batch Bayesian Optimization, improvements in the subjective evaluation of food quality can be achieved reliably, with very few trials and with the ability for bulk optimization. This study paves the way towards a broader vision of personalized food for taste-and-nutrition and transferable recipes

Modeling the Encoding of Saccade Kinematic Metrics in the Purkinje Cell Layer of the Cerebellar Vermis.

Recent electrophysiological observations related to saccadic eye movements in rhesus monkeys, suggest a prediction of the sensory consequences of movement in the Purkinje cell layer of the cerebellar oculomotor vermis (OMV). A definite encoding of real-time motion of the eye has been observed in simple-spike responses of the combined burst-pause Purkinje cell populations, organized based upon their complex-spike directional tuning. However, the underlying control mechanisms that could lead to such action encoding are still unclear. We propose a saccade control model, with emphasis on the structure of the OMV and its interaction with the extra-cerebellar components. In the simulated bilateral organization of the OMV, each caudal fastigial nucleus is arranged to receive incoming projections from combined burst-pause Purkinje cell populations. The OMV, through the caudal fastigial nuclei, interacts with the brainstem to provide adaptive saccade gain corrections that minimize the visual error in reaching a given target location. The simulation results corroborate the experimental Purkinje cell population activity patterns and their relation with saccade kinematic metrics. The Purkinje layer activity that emerges from the proposed organization, precisely predicted the speed of the eye at different target eccentricities. Simulated granular layer activity suggests no separate dynamics with respect to shaping the bilateral Purkine layer activity. We further examine the validity of the simulated OMV in maintaining the accuracy of saccadic eye movements in the presence of signal dependent variabilities, that can occur in extra-cerebellar pathways