Soft Morphological Computation

Soft Robotics is a relatively new area of research, where progress in material science has powered the next generation of robots, exhibiting biological-like properties such as soft/elastic tissues, compliance, resilience and more besides. One of the issues when employing soft robotics technologies is the soft nature of the interactions arising between the robot and its environment. These interactions are complex, and the their dynamics are non-linear and hard to capture with known models. In this thesis we argue that complex soft interactions can actually be beneficial to the robot, and give rise to rich stimuli which can be used for the resolution of robot tasks. We further argue that the usefulness of these interactions depends on statistical regularities, or structure, that appear in the stimuli. To this end, robots should appropriately employ their morphology and their actions, to influence the system-environment interactions such that structure can arise in the stimuli. In this thesis we show that learning processes can be used to perform such a task. Following this rationale, this thesis proposes and supports the theory of Soft Morphological Computation (SoMComp), by which a soft robot should appropriately condition, or ‘affect’, the soft interactions to improve the quality of the physical stimuli arising from it. SoMComp is composed of four main principles, i.e.: Soft Proprioception, Soft Sensing, Soft Morphology and Soft Actuation. Each of these principles is explored in the context of haptic object recognition or object handling in soft robots. Finally, this thesis provides an overview of this research and its future directions.AHDB CP17

Gaussian process inference modelling of dynamic robot control for expressive piano playing

Piano is a complex instrument, which humans learn to play after many years of practice. This paper investigates the complex dynamics of the embodied interactions between a human and piano, in order to gain insights into the nature of humans’ physical dexterity and adaptability. In this context, the dynamic interactions become particularly crucial for delicate expressions, often present in advanced music pieces, which is the main focus of this paper. This paper hypothesises that the relationship between motor control for key-pressing and the generated sound is a manifold problem, with high-degrees of non-linearity in nature. We employ a minimalistic experimental platform based on a robotic arm equipped with a single elastic finger in order to systematically investigate the motor control and resulting outcome of piano sounds. The robot was programmed to run 3125 key-presses on a physical digital piano with varied control parameters. The obtained data was applied to a Gaussian Process (GP) inference modelling method, to train a network in terms of 10 playing styles, corresponding to different expressions generated by a Musical Instrument Digital Interface (MIDI). By analysing the robot control parameters and the output sounds, the relationship was confirmed to be highly nonlinear, especially when the rich expressions (such as a broad range of sound dynamics) were necessary. Furthermore this relationship was difficult and time consuming to learn with linear regression models, compared to the developed GPbased approach. The performance of the robot controller was also compared to that of an experienced human player. The analysis shows that the robot is able to generate sounds closer to humans’ in some expressions, but requires additional investigations for othersEPSR

Soft Morphological Processing of Tactile Stimuli for Autonomous Category Formation

Sensor morphology is a fundamental aspect of tactile sensing technology. Design choices induce stimuli to be morphologically processed, changing the sensory perception of the touched objects and affecting inference at a later processing stage. We develop a framework to analyze the filtered sensor response and observe the correspondent change in tactile information. We test the morphological processing effects on the tactile stimuli by integrating a capacitive tactile sensor into a flat end-effector and creating three soft silicon-based filters with varying thickness (3mm, 6mm and 10mm). We incorporate the end-effector onto a robotic arm. We control the arm in order to apply a calibrated force onto 4 objects, and retrieve tactile images. We create an unsupervised inference process through the use of Principal Component Analysis and K-Means Clustering.We use the process to group the sensed objects into 2 classes and observe how different soft filters affect the clustering results. The sensor response with the 3mm soft filter allows for edges to be the feature with most variance (captured by PCA) and induces the association of edged objects. With thicker soft filters the associations change, and with a 10mm filter the sensor response results more diverse for objects with different elongation. We show that the clustering is intrinsically driven by the morphology of the sensor and that the robot’s world understanding changes according to it.This work was funded by the UK Agriculture and Horticulture Development Board and by The United Kingdom Engineering and Physical Sciences Research Council (EPSRC) MOTION grant [EP/N03211X/2]

Model-free Soft-Structure Reconstruction for Proprioception using Tactile Arrays

Continuum body structures provide unique opportunities for soft robotics, with the infinite degrees of freedom enabling unconstrained and highly adaptive exploration and manipulation. However, the infinite degrees of freedom of continuum bodies makes sensing (both intrinsically and extrinsically) challenging. To address this, in this paper we propose a model-free method for sensorizing tentacle-like continuum soft-structures using an array of spatially arranged capacitive tactile sensors. By using visual tracking, the relationship between the tactile response and the 3D shape of the continuum soft-structure can be learned. A data set of 15000 random soft-body postures was used, with recorded camera-tracked positions logged synchronously to the tactile sensor responses. This was used to train a neural network which can predict posture. We show it is possible to achieve proprioceptive awareness over all three axis of motion in space, reconstructing the body structure and inferring the soft body head’s pose with an average accuracy of ≈ 1mm in comparison to the visual tracked counterpart. To demonstrate the capabilities of the system, we perform random exploration of environments limiting the work-space of the sensorized robot. We find the method capable to autonomously reconstruct the reachable morphology of the environment without the need of external sensing units.This work was funded by the UK Agriculture and Horticulture Development Board (CP 172) and Physical Sciences Research Council (EPSRC) MOTION grant [EP/N03211X/2

Flexible, adaptive industrial assembly: driving innovation through competition

Funder: ArmAbstract: Robotics competitions stimulate the next generation of cutting edge robotics solutions and innovative technologies. The World Robot Summit (WRS) Industrial Assembly challenge posed a key research challenge: how to develop adaptive industrial assembly robots. The overall goal is to develop robots where minimal hardware or software changes are required to manufacture a new or altered product. This will minimise waste and allow the industry to move towards a far more flexible approach to manufacturing; this will provide exciting new technologies for the manufacturing industry and support many new business models and approaches. In this paper, we present an approach where general-purpose grippers and adaptive control approaches have been developed to move towards this research goal. These approaches enable highly flexible and adaptive assembly of a belt drive system. The abilities of this approach were demonstrated by taking part in the WRS Industrial Assembly Challenge. We achieved second place in the kitting challenge and second place in the adaptive manufacturing challenge and were presented with the Innovation Award

EDAMS: an Encoder-Decoder Architecture for Multi-grasp Soft sensing object recognition

The use of tactile sensing exhibits benefits over visual detection as it can be deployed in occluded environments and can provide deeper information about an object's material properties. Soft hands have increasingly been used for tactile object identification, providing a high degree of adaptability without requiring complex control schemes. In this work, we propose a framework for identifying a range of objects in any pose by exploiting the compliance of a soft hand equipped with distributed tactile sensing. We propose EDAMS, an Encoder-Decoder Architecture for Multi-grasp Soft sensing and an ad-hoc data structure capable of encoding information on multiple grasps, while decoupling the dependency on the pose order. We train the model to map the high-dimensional multi-grasp tactile sensor data into a lower-dimensional latent space capable of achieving the geometrical separation of each object class, and enabling accurate object classification. We provide an empirical analysis of the benefit of multi-grasp perception for object identification, and show its impact on the separation of the objects in sensor space. Notably, we find the classification accuracy to change widely across the number of grasps, ranging from 47.0% for a single grasp, to 99.9% for 10 grasps

Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks

Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to shortcut learning phenomena, where a model may rely on erroneous, easy-to-learn, cues while ignoring reliable ones. In this work, we propose an ensemble diversification framework exploiting the generation of synthetic counterfactuals using Diffusion Probabilistic Models (DPMs). We discover that DPMs have the inherent capability to represent multiple visual cues independently, even when they are largely correlated in the training data. We leverage this characteristic to encourage model diversity and empirically show the efficacy of the approach with respect to several diversification objectives. We show that diffusion-guided diversification can lead models to avert attention from shortcut cues, achieving ensemble diversity performance comparable to previous methods requiring additional data collection.Comment: Accepted at Neural Information Processing Systems(NeurIPS) 2023 - Workshop on Diffusion Model

Structuring of tactile sensory information for category formation in robotics palpation

Abstract: This paper proposes a framework to investigate the influence of physical interactions to sensory information, during robotic palpation. We embed a capacitive tactile sensor on a robotic arm to probe a soft phantom and detect and classify hard inclusions within it. A combination of PCA and K-Means clustering is used to: first, reduce the dimensionality of the spatiotemporal data obtained through the probing of each area in the phantom; second categorize the re-encoded data into a given number of categories. Results show that appropriate probing interactions can be useful in compensating for the quality of the data, or lack thereof. Finally, we test the proposed framework on a palpation scenario where a Support Vector Machine classifier is trained to discriminate amongst different types of hard inclusions. We show the proposed framework is capable of predicting the best-performing motion strategy, as well as the relative classification performance of the SVM classifier, solely based on unsupervised cluster analysis methods

Optical properties of highly nonlinear silicon-organic hybrid (SOH) waveguide geometries

Geometry, nonlinearity, dispersion and two-photon absorption figure of merit of three basic silicon-organic hybrid waveguide designs are compared. Four-wave mixing and heterodyne pump-probe measurements show that all designs achieve high nonlinearities. The fundamental limitation of two-photon absorption in silicon is overcome using silicon-organic hybrid integration, with a five-fold improvement for the figure of merit (FOM). The value of FOM = 2.19 measured for silicon-compatible nonlinear slot waveguides is the highest value published. (C) 2009 Optical Society of Americ

Non-Destructive Robotic Assessment of Mango Ripeness via Multi-Point Soft Haptics

To match the ever increasing standards of fresh products, and the need to reduce waste, we devise an alternative to the destructive and highly variable fruit ripeness estimation by a penetrometer. We propose a fully automatic method to assess the ripeness of mango which is non-destructive, allows the user to test multiple surface areas with a single touch and is capable of dissociating between ripe and non-ripe fruits. A custom-made gripper equipped with a capacitive tactile sensor array is used to palpate the fruit. The ripeness is estimated as mango stiffness extracted through a simplified spring model. We test the framework on a set of 25 mangoes of the Keitt variety, and compare the results to penetrometer measurements. We show it is possible to correctly classify 88% of the mango without removing the skin of the fruit. The method can be a valuable substitute for non-destructive fruit ripeness testing. To the authors knowledge, this is the first robotics ripeness estimation system based on capacitive tactile sensing technologyThis work was funded by the UK Agriculture and Horticulture Development Board (CP 172), Physical Sciences Research Council (EPSRC) MOTION grant [EP/N03211X/2] and Ministerio de Econom´ıa (DPI2015- 69041-R