Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure

Automating precision subtasks such as debridement (removing dead or diseased tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci Research Kit (dVRK) is challenging due to inherent non-linearities in cable-driven systems. We propose and evaluate a novel two-phase coarse-to-fine calibration method. In Phase I (coarse), we place a red calibration marker on the end effector and let it randomly move through a set of open-loop trajectories to obtain a large sample set of camera pixels and internal robot end-effector configurations. This coarse data is then used to train a Deep Neural Network (DNN) to learn the coarse transformation bias. In Phase II (fine), the bias from Phase I is applied to move the end-effector toward a small set of specific target points on a printed sheet. For each target, a human operator manually adjusts the end-effector position by direct contact (not through teleoperation) and the residual compensation bias is recorded. This fine data is then used to train a Random Forest (RF) to learn the fine transformation bias. Subsequent experiments suggest that without calibration, position errors average 4.55mm. Phase I can reduce average error to 2.14mm and the combination of Phase I and Phase II can reduces average error to 1.08mm. We apply these results to debridement of raisins and pumpkin seeds as fragment phantoms. Using an endoscopic stereo camera with standard edge detection, experiments with 120 trials achieved average success rates of 94.5%, exceeding prior results with much larger fragments (89.4%) and achieving a speedup of 2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source code, data, and videos are available at https://sites.google.com/view/calib-icra/.Comment: Code, data, and videos are available at https://sites.google.com/view/calib-icra/. Final version for ICRA 201

O papel da inovação como factor de sucesso no mercado competitivo actual : um estudo aplicado à Portugal Telecom

Mestrado em Gestão e EmpreendedorismoEstando as organizações sujeitas a uma cada vez maior concorrência na maioria dos mercados graças ao fenómeno da globalização, estas terão de adoptar novas estratégias de modo a que a concorrência não seja um entrave ao seu desenvolvimento e ao seu sucesso futuro. Com tudo isto as organizações têm que ter consciência que só através de uma aposta centrada na inovação conseguirão diferenciarem-se dos demais concorrentes e assim sobreviver aos mercados altamente competitivos e aos consumidores cada vez mais exigentes. Este estudo tem assim como principal fundamento demonstrar que a principal aliada das organizações no Século XXI é claramente a inovação, conseguindo directamente com esta aposta melhorar as suas performances gerais em termos de resultados de um conjunto importante de indicadores que lhes permitirão aumentar a sua competitividade. Contudo para implementar uma inovação no mercado primeiro, a liderança da organização terá de fomentar internamente uma cultura de inovação com traços característicos e com isto desenvolver um determinado tipo de inovação tendo em conta o mercado em que se querem inserir e consoante os objectivos da própria organização. De modo a cumprir tal fundamento será analisado como objecto de estudo o caso do lançamento do conceito MEO por parte da Portugal Telecom (PT) que permitiu a esta organização melhorar de uma maneira bastante considerável os seus resultados em termos de receitas, número de clientes e de relevância no mercado, situação que não aconteceria certamente caso o serviço prestado pela MEO não apresentasse funcionalidades inovadoras em comparação com as existentes neste mercado.Once the organisations are subject to an ever increasing competition in most markets due to the phenomenon of globalisation, they will have to adopt new strategies in such a way that competition does not become an hindrance to their development and their success in the future. With this in mind the organisations will have to be recognizant that only through an effort centred on innovation will they be able to differentiate themselves from the competitors and thus survive the highly competitive markets and the ever more demanding consumers. This study has its main concern in displaying that the main ally of XXI century organisations is clearly innovation, that through innovation the organisations become able to improve their general performance in various important indicators that allows them to increase their competitiveness. But, in order to implement an innovation in the market, the leadership of a given organisation will have to implement an internal innovation culture first, with its very own characteristic traces, and in this way develop a specific kind of innovation considering the market in which they want to penetrate. In order to achieve the main goal, the object of study to be analyzed will be the launching of the concept MEO by Portugal Telecom (PT) which allowed this organisation to improve by a considerable margin its results in matters of revenue, number of clients and relevancy in the market, a situation that certainly would not happen if the service offered by MEO did not include innovative functionalities in comparison to those existing in the market

Learning to Singulate Layers of Cloth using Tactile Feedback

Robotic manipulation of cloth has applications ranging from fabrics manufacturing to handling blankets and laundry. Cloth manipulation is challenging for robots largely due to their high degrees of freedom, complex dynamics, and severe self-occlusions when in folded or crumpled configurations. Prior work on robotic manipulation of cloth relies primarily on vision sensors alone, which may pose challenges for fine-grained manipulation tasks such as grasping a desired number of cloth layers from a stack of cloth. In this paper, we propose to use tactile sensing for cloth manipulation; we attach a tactile sensor (ReSkin) to one of the two fingertips of a Franka robot and train a classifier to determine whether the robot is grasping a specific number of cloth layers. During test-time experiments, the robot uses this classifier as part of its policy to grasp one or two cloth layers using tactile feedback to determine suitable grasping points. Experimental results over 180 physical trials suggest that the proposed method outperforms baselines that do not use tactile feedback and has better generalization to unseen cloth compared to methods that use image classifiers. Code, data, and videos are available at https://sites.google.com/view/reskin-cloth.Comment: IROS 2022. See https://sites.google.com/view/reskin-cloth for supplementary materia

Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks

Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional configuration spaces of deformables, compared to rigid objects, make manipulation difficult not only for multi-step planning, but even for goal specification. Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag". In this work, we develop a suite of simulated benchmarks with 1D, 2D, and 3D deformable structures, including tasks that involve image-based goal-conditioning and multi-step deformable manipulation. We propose embedding goal-conditioning into Transporter Networks, a recently proposed model architecture for learning robotic manipulation that rearranges deep features to infer displacements that can represent pick and place actions. We demonstrate that goal-conditioned Transporter Networks enable agents to manipulate deformable structures into flexibly specified configurations without test-time visual anchors for target locations. We also significantly extend prior results using Transporter Networks for manipulating deformable objects by testing on tasks with 2D and 3D deformables. Supplementary material is available at https://berkeleyautomation.github.io/bags/.Comment: See https://berkeleyautomation.github.io/bags/ for project website and code; v2 corrects some BibTeX entries, v3 is ICRA 2021 version (minor revisions