Considering the anchoring problem in robotic intelligent bin picking

Random Bin Picking means the selection by a robot of a particular item from a container (or bin) in which there are many items randomly distributed. Generalist robots and the Anchoring Problem should be considered if we want to provide a more general solution, since users want that it works with different type of items that are not known 'a priori'. Therefore, we are working on an approach in which robot learning and human-robot interaction are used to anchor control primitives and robot skills to objects and action symbols while the robot system is running, but we are limiting the scope to the packaging domain. In this paper we explain how to use our system to do anchoring in Robotic Bin Picking.Peer ReviewedPostprint (author's final draft

Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application

Interpretable task planning and learning for autonomous robotic surgery with logic programming

This thesis addresses the long-term goal of full (supervised) autonomy in surgery, characterized by dynamic environmental (anatomical) conditions, unpredictable workflow of execution and workspace constraints. The scope is to reach autonomy at the level of sub-tasks of a surgical procedure, i.e. repetitive, yet tedious operations (e.g., dexterous manipulation of small objects in a constrained environment, as needle and wire for suturing). This will help reducing time of execution, hospital costs and fatigue of surgeons during the whole procedure, while further improving the recovery time for the patients. A novel framework for autonomous surgical task execution is presented in the first part of this thesis, based on answer set programming (ASP), a logic programming paradigm, for task planning (i.e., coordination of elementary actions and motions). Logic programming allows to directly encode surgical task knowledge, representing emph{plan reasoning methodology} rather than a set of pre-defined plans. This solution introduces several key advantages, as reliable human-like interpretable plan generation, real-time monitoring of the environment and the workflow for ready adaptation and failure recovery. Moreover, an extended review of logic programming for robotics is presented, motivating the choice of ASP for surgery and providing an useful guide for robotic designers. In the second part of the thesis, a novel framework based on inductive logic programming (ILP) is presented for surgical task knowledge learning and refinement. ILP guarantees fast learning from very few examples, a common drawback of surgery. Also, a novel action identification algorithm is proposed based on automatic environmental feature extraction from videos, dealing for the first time with small and noisy datasets collecting different workflows of executions under environmental variations. This allows to define a systematic methodology for unsupervised ILP. All the results in this thesis are validated on a non-standard version of the benchmark training ring transfer task for surgeons, which mimics some of the challenges of real surgery, e.g. constrained bimanual motion in small space

Study of the anchoring problem in generalist robots based on ROSPlan

Trabajo presentado a la 19th International Conference of the Catalan Association for Artificial Intelligence (CCIA), celebrada en Barcelona (España) del 19 al 21 de octubre de 2016.In real life environments where robots must deal with complex situations and humans, generalist robots that adapt to novel situations are needed. They are composed by two sub-systems: perception/actuation and knowledge representation, and they need that symbols in the high-level area are coupled to objects and actions of the low-level area. This is the so-called Anchoring Problem. In this paper we present the system we are using to study this problem. It is based on ROSPlan, a framework that provides a generic method for task planning in a ROS system. The high-level area is composed by a planner that uses PDDL files and a knowledge representation system, while the low-level area is defined as a set of robot services exported using ROS actions, services and topics. We plan to contribute to this problem by applying human-robot interaction and learning techniques, and our main objectives are: (1) link an existing symbol with a learned action by interaction, and (2) automated code generation of ad-hoc ROS nodes that connect symbols to specific perceptions/actions.This work was partially funded by Spanish research project DPI2013-42458-PPeer Reviewe