Combining task and motion planning for mobile manipulators

Aplicat embargament des de la data de defensa fins el dia 31/12/2019Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit d’Enginyeria IndustrialThis thesis addresses the combination of task and motion planning which deals with different types of robotic manipulation problems. Manipulation problems are referred to as mobile manipulation, collaborative multiple mobile robots tasks, and even higher dimensional tasks (like bi-manual robots or mobile manipulators). Task and motion planning problems needs to obtain a geometrically feasible manipulation plan through symbolic and geometric search space. The combination of task and motion planning levels has emerged as a challenging issue as the failure leads robots to dead-end tasks due to geometric constraints. In addition, task planning is combined with physics-based motion planning and information to cope with manipulation tasks in which interactions between robots and objects are required, or also a low-cost feasible plan in terms of power is looked for. Moreover, combining task and motion planning frameworks is enriched by introducing manipulation knowledge. It facilitates the planning process and aids to provide the way of executing symbolic actions. Combining task and motion planning can be considered under uncertain information and with human-interaction. Uncertainty can be viewed in the initial state of the robot world or the result of symbolic actions. To deal with such issues, contingent-based task and motion planning is proposed using a perception system and human knowledge. Also, robots can ask human for those tasks which are difficult or infeasible for the purpose of collaboration. An implementation framework to combine different types of task and motion planning is presented. All the required modules and tools are also illustrated. As some task planning algorithms are implemented in Prolog or C++ languages and our geometric reasoner is developed in C++, the flow of information between different languages is explained.Aquesta tesis es centra en les eines de planificació combinada a nivell de tasca i a nivell de moviments per abordar diferents problemes de manipulació robòtica. Els problemes considerats són de navegació de robots mòbil enmig de obstacles no fixes, tasques de manipulació cooperativa entre varis robots mòbils, i tasques de manipulació de dimensió més elevada com les portades a terme amb robots bi-braç o manipuladors mòbils. La planificació combinada de tasques i de moviments ha de cercar un pla de manipulació que sigui geomètricament realitzable, a través de d'un espai de cerca simbòlic i geomètric. La combinació dels nivells de planificació de tasca i de moviments ha sorgit com un repte ja que les fallades degudes a les restriccions geomètriques poden portar a tasques sense solució. Addicionalment, la planificació a nivell de tasca es combina amb informació de la física de l'entorn i amb mètodes de planificació basats en la física, per abordar tasques de manipulació en les que la interacció entre el robot i els objectes és necessària, o també si es busca un pla realitzable i amb un baix cost en termes de potència. A més, el marc proposat per al combinació de la planificació a nivell de tasca i a nivell de moviments es millora mitjançant l'ús de coneixement, que facilita el procés de planificació i ajuda a trobar la forma d'executar accions simbòliques. La combinació de nivells de planificació també es pot considerar en casos d'informació incompleta i en la interacció humà-robot. La incertesa es considera en l'estat inicial i en el resultat de les accions simbòliques. Per abordar aquest problema, es proposa la planificació basada en contingències usant un sistema de percepció i el coneixement de l'operari humà. Igualment, els robots poden demanar col·laboració a l'operari humà per a que realitzi aquelles accions que són difícils o no realitzables pel robot. Es presenta també un marc d'implementació per a la combinació de nivells de planificació usant diferents mètodes, incloent tots els mòduls i eines necessàries. Com que alguns algorismes estan implementats en Prolog i d'altres en C++, i el mòdul de raonament geomètric proposat està desenvolupat en C++, es detalla el flux d'informació entre diferents llenguatges.Award-winningPostprint (published version

Assistance strategies for robotized laparoscopy

Robotizing laparoscopic surgery not only allows achieving better accuracy to operate when a scale factor is applied between master and slave or thanks to the use of tools with 3 DoF, which cannot be used in conventional manual surgery, but also due to additional informatic support. Relying on computer assistance different strategies that facilitate the task of the surgeon can be incorporated, either in the form of autonomous navigation or cooperative guidance, providing sensory or visual feedback, or introducing certain limitations of movements. This paper describes different ways of assistance aimed at improving the work capacity of the surgeon and achieving more safety for the patient, and the results obtained with the prototype developed at UPC.Peer ReviewedPostprint (author's final draft

Robot Assisted Object Manipulation for Minimally Invasive Surgery

Robotic systems have an increasingly important role in facilitating minimally invasive surgical treatments. In robot-assisted minimally invasive surgery, surgeons remotely control instruments from a console to perform operations inside the patient. However, despite the advanced technological status of surgical robots, fully autonomous systems, with decision-making capabilities, are not yet available. In 2017, a structure to classify the research efforts toward autonomy achievable with surgical robots was proposed by Yang et al. Six different levels were identified: no autonomy, robot assistance, task autonomy, conditional autonomy, high autonomy, and full autonomy. All the commercially available platforms in robot-assisted surgery is still in level 0 (no autonomy). Despite increasing the level of autonomy remains an open challenge, its adoption could potentially introduce multiple benefits, such as decreasing surgeons’ workload and fatigue and pursuing a consistent quality of procedures. Ultimately, allowing the surgeons to interpret the ample and intelligent information from the system will enhance the surgical outcome and positively reflect both on patients and society. Three main aspects are required to introduce automation into surgery: the surgical robot must move with high precision, have motion planning capabilities and understand the surgical scene. Besides these main factors, depending on the type of surgery, there could be other aspects that might play a fundamental role, to name some compliance, stiffness, etc. This thesis addresses three technological challenges encountered when trying to achieve the aforementioned goals, in the specific case of robot-object interaction. First, how to overcome the inaccuracy of cable-driven systems when executing fine and precise movements. Second, planning different tasks in dynamically changing environments. Lastly, how the understanding of a surgical scene can be used to solve more than one manipulation task. To address the first challenge, a control scheme relying on accurate calibration is implemented to execute the pick-up of a surgical needle. Regarding the planning of surgical tasks, two approaches are explored: one is learning from demonstration to pick and place a surgical object, and the second is using a gradient-based approach to trigger a smoother object repositioning phase during intraoperative procedures. Finally, to improve scene understanding, this thesis focuses on developing a simulation environment where multiple tasks can be learned based on the surgical scene and then transferred to the real robot. Experiments proved that automation of the pick and place task of different surgical objects is possible. The robot was successfully able to autonomously pick up a suturing needle, position a surgical device for intraoperative ultrasound scanning and manipulate soft tissue for intraoperative organ retraction. Despite automation of surgical subtasks has been demonstrated in this work, several challenges remain open, such as the capabilities of the generated algorithm to generalise over different environment conditions and different patients

Learning-based robotic manipulation for dynamic object handling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronic Engineering at the School of Food and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand

Figures are re-used in this thesis with permission of their respective publishers or under a Creative Commons licence.Recent trends have shown that the lifecycles and production volumes of modern products are shortening. Consequently, many manufacturers subject to frequent change prefer flexible and reconfigurable production systems. Such schemes are often achieved by means of manual assembly, as conventional automated systems are perceived as lacking flexibility. Production lines that incorporate human workers are particularly common within consumer electronics and small appliances. Artificial intelligence (AI) is a possible avenue to achieve smart robotic automation in this context. In this research it is argued that a robust, autonomous object handling process plays a crucial role in future manufacturing systems that incorporate robotics—key to further closing the gap between manual and fully automated production. Novel object grasping is a difficult task, confounded by many factors including object geometry, weight distribution, friction coefficients and deformation characteristics. Sensing and actuation accuracy can also significantly impact manipulation quality. Another challenge is understanding the relationship between these factors, a specific grasping strategy, the robotic arm and the employed end-effector. Manipulation has been a central research topic within robotics for many years. Some works focus on design, i.e. specifying a gripper-object interface such that the effects of imprecise gripper placement and other confounding control-related factors are mitigated. Many universal robotic gripper designs have been considered, including 3-fingered gripper designs, anthropomorphic grippers, granular jamming end-effectors and underactuated mechanisms. While such approaches have maintained some interest, contemporary works predominantly utilise machine learning in conjunction with imaging technologies and generic force-closure end-effectors. Neural networks that utilise supervised and unsupervised learning schemes with an RGB or RGB-D input make up the bulk of publications within this field. Though many solutions have been studied, automatically generating a robust grasp configuration for objects not known a priori, remains an open-ended problem. An element of this issue relates to a lack of objective performance metrics to quantify the effectiveness of a solution—which has traditionally driven the direction of community focus by highlighting gaps in the state-of-the-art. This research employs monocular vision and deep learning to generate—and select from—a set of hypothesis grasps. A significant portion of this research relates to the process by which a final grasp is selected. Grasp synthesis is achieved by sampling the workspace using convolutional neural networks trained to recognise prospective grasp areas. Each potential pose is evaluated by the proposed method in conjunction with other input modalities—such as load-cells and an alternate perspective. To overcome human bias and build upon traditional metrics, scores are established to objectively quantify the quality of an executed grasp trial. Learning frameworks that aim to maximise for these scores are employed in the selection process to improve performance. The proposed methodology and associated metrics are empirically evaluated. A physical prototype system was constructed, employing a Dobot Magician robotic manipulator, vision enclosure, imaging system, conveyor, sensing unit and control system. Over 4,000 trials were conducted utilising 100 objects. Experimentation showed that robotic manipulation quality could be improved by 10.3% when selecting to optimise for the proposed metrics—quantified by a metric related to translational error. Trials further demonstrated a grasp success rate of 99.3% for known objects and 98.9% for objects for which a priori information is unavailable. For unknown objects, this equated to an improvement of approximately 10% relative to other similar methodologies in literature. A 5.3% reduction in grasp rate was observed when removing the metrics as selection criteria for the prototype system. The system operated at approximately 1 Hz when contemporary hardware was employed. Experimentation demonstrated that selecting a grasp pose based on the proposed metrics improved grasp rates by up to 4.6% for known objects and 2.5% for unknown objects—compared to selecting for grasp rate alone. This project was sponsored by the Richard and Mary Earle Technology Trust, the Ken and Elizabeth Powell Bursary and the Massey University Foundation. Without the financial support provided by these entities, it would not have been possible to construct the physical robotic system used for testing and experimentation. This research adds to the field of robotic manipulation, contributing to topics on grasp-induced error analysis, post-grasp error minimisation, grasp synthesis framework design and general grasp synthesis. Three journal publications and one IEEE Xplore paper have been published as a result of this research

Sense, Think, Grasp: A study on visual and tactile information processing for autonomous manipulation

Interacting with the environment using hands is one of the distinctive abilities of humans with respect to other species. This aptitude reflects on the crucial role played by objects\u2019 manipulation in the world that we have shaped for us. With a view of bringing robots outside industries for supporting people during everyday life, the ability of manipulating objects autonomously and in unstructured environments is therefore one of the basic skills they need. Autonomous manipulation is characterized by great complexity especially regarding the processing of sensors information to perceive the surrounding environment. Humans rely on vision for wideranging tridimensional information, prioprioception for the awareness of the relative position of their own body in the space and the sense of touch for local information when physical interaction with objects happens. The study of autonomous manipulation in robotics aims at transferring similar perceptive skills to robots so that, combined with state of the art control techniques, they could be able to achieve similar performance in manipulating objects. The great complexity of this task makes autonomous manipulation one of the open problems in robotics that has been drawing increasingly the research attention in the latest years. In this work of Thesis, we propose possible solutions to some key components of autonomous manipulation, focusing in particular on the perception problem and testing the developed approaches on the humanoid robotic platform iCub. When available, vision is the first source of information to be processed for inferring how to interact with objects. The object modeling and grasping pipeline based on superquadric functions we designed meets this need, since it reconstructs the object 3D model from partial point cloud and computes a suitable hand pose for grasping the object. Retrieving objects information with touch sensors only is a relevant skill that becomes crucial when vision is occluded, as happens for instance during physical interaction with the object. We addressed this problem with the design of a novel tactile localization algorithm, named Memory Unscented Particle Filter, capable of localizing and recognizing objects relying solely on 3D contact points collected on the object surface. Another key point of autonomous manipulation we report on in this Thesis work is bi-manual coordination. The execution of more advanced manipulation tasks in fact might require the use and coordination of two arms. Tool usage for instance often requires a proper in-hand object pose that can be obtained via dual-arm re-grasping. In pick-and-place tasks sometimes the initial and target position of the object do not belong to the same arm workspace, then requiring to use one hand for lifting the object and the other for locating it in the new position. At this regard, we implemented a pipeline for executing the handover task, i.e. the sequences of actions for autonomously passing an object from one robot hand on to the other. The contributions described thus far address specific subproblems of the more complex task of autonomous manipulation. This actually differs from what humans do, in that humans develop their manipulation skills by learning through experience and trial-and-error strategy. Aproper mathematical formulation for encoding this learning approach is given by Deep Reinforcement Learning, that has recently proved to be successful in many robotics applications. For this reason, in this Thesis we report also on the six month experience carried out at Berkeley Artificial Intelligence Research laboratory with the goal of studying Deep Reinforcement Learning and its application to autonomous manipulation

An Architecture for Online Affordance-based Perception and Whole-body Planning

The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robot's sensor suite transmitted over a constrained, field-realistic communications link. We describe the design considerations, architecture, implementation and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule