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Robots capable of assisting elderly people in their homes will become indispensable, since the world population is aging at an alarming rate. A crucial requirement for these robotic caregivers will be the ability to safely interact with humans, such as firmly grasping a human arm without applying excessive force. Minding this concern, we developed a reactive grasp that, using tactile sensors, monitors the pressure it exerts during manipulation. Our approach, inspired by human manipulation, employs an architecture based on different grasping phases that represent particular stages in a manipulation task. Within these phases, we implemented and composed simple components to interpret and react to the information obtained by the tactile sensors. Empirical results, using a Care-O-bot 3® with a Schunk Dexterous Hand (SDH-2), show that considering tactile information can reduce the force exerted on the objects significantly.
This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot.We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation and robust object recognition.
Software development for robots is a knowledgeintensive exercise. To capture this knowledge explicitly and formally in the form of various domain models, roboticists have recently employed model-driven engineering (MDE) approaches. However, these models are merely seen as a way to support humans during the robot's software design process. We argue that the robots themselves should be first-class consumers of this knowledge to autonomously adapt their software to the various and changing run-time requirements induced, for instance, by the robot's tasks or environment. Motivated by knowledge-enabled approaches, we address this problem by employing a graph-based knowledge representation that allows us not only to persistently store domain models, but also to formulate powerful queries for the sake of run time adaptation. We have evaluated our approach in an integrated, real-world system using the neo4j graph database and we report some lessons learned. Further, we show that the graph database imposes only little overhead on the system's overall performance.
We present the design and development of a benchmarking testbed for the Factory of the Future. The testbed as a physical installation enables to study, compare and assess robotics scenarios involving the integration of mobile robots and manipulators with automation equipment, large-scale integration of service robots and industrial robots, cohabitation of robots and humans, and cooperation of multiple robots and/or humans. We also report on the lessons learned of using the testbed in recent robotic competitions.
Robot programming is an interdisciplinary and knowledge-intensive task. All too often, knowledge of the different robotics domains remains implicit. Although, this is slowly changing with the rising interest in explicit knowledge representations through domain-specific languages (DSL), very little is known about the DSL design and development processes themselves. To this end, we present and discuss the reverse-engineered process from the development of our Grasp Domain Definition Language (GDDL), a declarative DSL for the explicit specification of grasping problems. An important finding is that the process comprises similar building blocks as existing software development processes, like the Unified Process.
Grasping objects and using them in a task-oriented manner is challenging for a robot. It requires an understanding of the object, the robot's capabilities, and the task to be executed. We argue that an explicit representation of these domains increases reusability and robustness of the resulting system. We present the declarative Grasp Domain Definition Language (GDDL) which enables the explicit grasp-planner-independent specification of grasping problems. The formal model underlying GDDL enables the definition of formal constraints that are validated during design time and run time. Our approach has been realized on two real robots.
The RoCKIn@Work Challenge
(2014)
RoCKIn is a EU-funded project aiming to foster scientific progress and innovation in cognitive systems and robotics through the design and implementation of competitions. An additional objective of RoCKIn is to increase public awareness of the current state-of-the-art in robotics in Europe and to demonstrate the innovation potential of robotics applications for solving societal challenges and improving the competitiveness of Europe in the global markets. In order to achieve these objectives, RoCKIn develops two competitions, one for domestic service robots (RoCKIn@Home) and one for industrial robots in factories (RoCKIn@Work). These competitions are designed around challenges that are based on easy-to-communicate and convincing user stories, which catch the interest of both the general public and the scientific community. The latter is in particular interested in solving open scientific challenges and to thoroughly assess, compare, and evaluate the developed approaches with competing ones. To allow this to happen, the competitions are designed to meet the requirements of benchmarking procedures and good experimental methods. The integration of benchmarking technology with the competition concept is one of the main objectives of RoCKIn.
The RoCKIn@Home Challenge
(2014)
RoCKIn is a EU-funded project aiming to foster scientific progress and innovation in cognitive systems and robotics through the design and implementation of competitions, to increase public awareness of the current state-of-the-art in robotics in Europe, and to demonstrate the innovation potential of robotics applications for solving societal challenges and improving the competitiveness of Europe in the global markets. RoCKIn develops two competitions, one for domestic service robots (RoCKIn@Home) and one for industrial robots in factories (RoCKIn@Work). The integration of benchmarking technology with the competition concept is one of the main objectives of RoCKIn.
Mobile manipulators are viewed as an essential component for making the factory of the future become a reality. RoboCup@Work is a competition designed by a group of researchers from the RoboCup community and focuses on the use of mobile manipulators and their integration with automation equipment for performing industrially-relevant tasks. The paper describes the design and implementation of the competition and the experiences made so far.
This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot. We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation and robust object recognition.
Knowledge-Based Instrumentation and Control for Competitive Industry-Inspired Robotic Domains
(2016)
Compliant manipulation is a crucial skill for robots when they are supposed to act as helping hands in everyday household tasks. Still, nowadays, those skills are hand-crafted by experts which frequently requires labor-intensive, manual parameter tuning. Moreover, some tasks are too complex to be specified fully using a task specification. Learning these skills, by contrast, requires a high number of costly and potentially unsafe interactions with the environment. We present a compliant manipulation approach using reinforcement learning guided by the Task Frame Formalism, a task specification method. This allows us to specify the easy to model knowledge about a task while the robot learns the unmodeled components by reinforcement learning. We evaluate the approach by performing a compliant manipulation task with a KUKA LWR 4+ manipulator. The robot was able to learn force control policies directly on the robot without using any simulation.
Design of a declarative language for task-oriented grasping and tool-use with dextrous robotic hands
(2014)
Apparently simple manipulation tasks for a human such as transportation or tool use are challenging to replicate in an autonomous service robot. Nevertheless, dextrous manipulation is an important aspect for a robot in many daily tasks. While it is possible to manufacture special-purpose hands for one specific task in industrial settings, a generalpurpose service robot in households must have flexible hands which can adapt to many tasks. Intelligently using tools enables the robot to perform tasks more efficiently and even beyond the designed capabilities. In this work a declarative domain-specific language, called Grasp Domain Definition Language (GDDL), is presented that allows the specification of grasp planning problems independently of a specific grasp planner. This design goal resembles the idea of the Planning Domain Definition Language (PDDL). The specification of GDDL requires a detailed analysis of the research in grasping in order to identify best practices in different domains that contribute to a grasp. These domains describe for instance physical as well as semantic properties of objects and hands. Grasping always has a purpose which is captured in the task domain definition. It enables the robot to grasp an object in a taskdependent manner. Suitable representations in these domains have to be identified and formalized for which a domain-driven software engineering approach is applied. This kind of modeling allows the specification of constraints which guide the composition of domain entity specifications. The domain-driven approach fosters reuse of domain concepts while the constraints enable the validation of models already during design time. A proof of concept implementation of GDDL into the GraspIt! grasp planner is developed. Preliminary results of this thesis have been published and presented on the IEEE International Conference on Robotics and Automation (ICRA).
RoCKIn@Work was focused on benchmarks in the domain of industrial robots. Both task and functionality benchmarks were derived from real world applications. All of them were part of a bigger user story painting the picture of a scaled down real world factory scenario. Elements used to build the testbed were chosen from common materials in modern manufacturing environments. Networked devices, machines controllable through a central software component, were also part of the testbed and introduced a dynamic component to the task benchmarks. Strict guidelines on data logging were imposed on participating teams to ensure gathered data could be automatically evaluated. This also had the positive effect that teams were made aware of the importance of data logging, not only during a competition but also during research as useful utility in their own laboratory. Tasks and functionality benchmarks are explained in detail, starting with their use case in industry, further detailing their execution and providing information on scoring and ranking mechanisms for the specific benchmark.
