Luca Scimeca

Teleoperated medical robots have the potential to revolutionize healthcare. However, when developing systems for tasks like remote palpation, state-of-the-art literature still uses test phantoms of oversimplified geometries, due to the complexity of the required mechanical robot-patient interaction. In reality, human bodies have complex 3D shapes and require fine-tuning of all 6 manipulator's degrees of freedom, controlled by the user. In this paper, we argue that the implementation of depth-vision-driven autonomous dimensionality-reduction (DVD ADR) shared control can greatly improve the users' performance. The proposed control method keeps the user in control of the end-effector's position, while automatically adjusting its orientation in order to maintain the tactile sensor normal to the phantom's surface. A depth camera and a computer vision algorithm are used to infer the phantom's shape and achieve DVD ADR shared control. Experimental results showcase how this leads to statistically significant performance improvement. Not only were the participants able to achieve more precise palpations, with up to $29.5\%$ and $22.4\%$ more accuracy in position and orientation, respectively, but the DVD ADR shared control allowed them to achieve a $8.8\%$ better detection accuracy while needing $13.8\%$ less time. The above-mentioned results are all tested for statistical significance and achieved a p-value lower than 0.05.

Robotic phantoms enable advanced physical examination training before using human patients. In this article, we present an abdominal phantom for palpation training with controllable stiffness liver nodules that can also sense palpation forces. The coupled sensing and actuation approach is achieved by pneumatic control of positive-granular jammed nodules for tunable stiffness. Soft sensing is done using the variation of internal pressure of the nodules under external forces. This article makes original contributions to extend the linear region of the neo-Hookean characteristic of the mechanical behavior of the nodules by 140% compared to no-jamming conditions and to propose a method using the organ level controllable nodules as sensors to estimate palpation position and force with a root-mean-square error of 4% and 6.5%, respectively. Compared to conventional soft sensors, the method allows the phantom to sense with no interference to the simulated physiological conditions when providing quantified feedback to trainees, and to enable training following current bare-hand examination protocols without the need to wear data gloves to collect data.

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 GP-based 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 others.

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.

Morphology been shown to be a fundamental aspect of tactile sensing in soft robotics, one that can aid, and indeed enable, complex discrimination tasks. For a robot to change its sensor morphology as well as control appropriately, the parametric search over morphology and control parameters is usually slow and unsuited for real-world applications. We develop a framework based on Bayesian Exploration, to allow a robot to co-optimize both changes in tactile sensing morphology and robot action control, to aid in complex tactile object discrimination tasks. We test the framework by performing object discrimination on a set of eight objects, varying three different physical properties: geometry, surface texture, and stiffness. We integrate a capacitive tactile sensor into a flat end-effector and create three soft silicon-based filters with varying morphological properties. We incorporate the end-effector onto a robotic arm and perform repetitive, parameterized touch experiments, on each object. We show morphing is indeed necessary to dissociate amongst different object properties with the sensor at hand. Moreover, we show the proposed framework can consistently achieve optimal morphology-action configurations in approximately half the time than systematic search over parameters. This work marks a step towards the creation of robots capable of using morphology and action control to actively aid in discrimination tasks.

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.

The use of robotic technologies in agriculture is becoming increasingly established; in particular focusing on harvesting. Some produce, such as iceberg lettuce, require additional processing to produce supermarket ready product. In the case of lettuce, the outer leaves must be removed after harvesting, posing a challenging vision and manipulation task which is currently performed by humans. The scenario considered is a lettuce placed in a random pose on a flat table where the outermost leaves must be peeled quickly without damaging the lettuce. The paper presents the development of a novel machine vision pipeline for orientation estimation of the lettuce and detection of the stem. Furthermore, we present the architecture of a vacuum system design and a robot control procedure for the complete removal of the outer leaves in under 30 seconds. To the author's knowledge, this is the first robotic lettuce leaf peeling system.