Arne Nordmann

A user study on kinesthetic teaching of redundant robots in task and configuration space

The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide a great degree of flexibility for the realization of complex applications, the flexibility gained generates the need for additional modeling steps and definition of criteria for redundancy resolution constraining the robots movement generation. The explicit modeling of such criteria usually require experts to adapt the robots movement generation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching by guiding the robot to implicitly model the specific constraints in task and configuration space. We argue that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and show that typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, we propose a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. We evaluated this approach in a user study with 49 industrial workers at HARTING, a medium-sized manufacturing company. The results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space, and yield significantly improved performance for the teach-in of trajectories in task space.

Software abstractions for simulation and control of a continuum robot

The Bionic Handling Assistant is a new continuum robot which is manufactured in a rapid-prototyping procedure out of elastic polyamide. Its mechanical flexibility and low weight provide an enormous potential for physical human robot interaction. Yet, the elasticity and parallel continuum actuation design challenge standard approaches to deal with a robot from a control, simulation, and software modeling perspective. We investigate how the software abstractions of the existing Robot Control Interface (RCI) and the Compliant Control Architecture (CCA) can deal with this platform from a software modeling and software architectural perspective. We focus on three different challenges: the first challenge is to enable reasonable and hierarchical semantic abstractions of the robot. The second challenge is to develop hardware I/O abstractions for the prototypical and heterogeneous technical setup. The third challenge is to realize this in a flexible and reusable manner. We evaluate our approaches to the above challenges in a practical scenario in which the robot is controlled either in simulation or on the real robot.

Teaching nullspace constraints in physical human-robot interaction using Reservoir Computing

A major goal of current robotics research is to enable robots to become co-workers that collaborate with humans efficiently and adapt to changing environments or workflows. We present an approach utilizing the physical interaction capabilities of compliant robots with data-driven and model-free learning in a coherent system in order to make fast reconfiguration of redundant robots feasible. Users with no particular robotics knowledge can perform this task in physical interaction with the compliant robot, for example to reconfigure a work cell due to changes in the environment. For fast and efficient learning of the respective null-space constraints, a reservoir neural network is employed. It is embedded in the motion controller of the system, hence allowing for execution of arbitrary motions in task space. We describe the training, exploration and the control architecture of the systems as well as present an evaluation on the KUKA Light-Weight Robot. Our results show that the learned model solves the redundancy resolution problem under the given constraints with sufficient accuracy and generalizes to generate valid joint-space trajectories even in untrained areas of the workspace.

Modeling of movement control architectures based on motion primitives using domain-specific languages

This paper introduces a model-driven approach for engineering complex movement control architectures based on motion primitives, which in recent years have been a central development towards adaptive and flexible control of complex and compliant robots. We consider rich motor skills realized through the composition of motion primitives as our domain. In this domain we analyze the control architectures of representative example systems to identify common abstractions. It turns out that the introduced notion of motion primitives implemented as dynamical systems with machine learning capabilities, provide the computational building block for a large class of such control architectures. Building on the identified concepts, we introduce domain-specific languages that allow the compact specification of movement control architectures based on motion primitives and their coordination respectively. Using a proper tool chain, we show how to employ this model-driven approach in a case study for the real world example of automatic laundry grasping with the KUKA LWR-IV, where executable source-code is automatically generated from the domain-specific language specification.

The Cognitive Interaction Toolkit Improving Reproducibility of Robotic Systems Experiments

Research on robot systems either integrating a large number of capabilities in a single architecture or displaying outstanding performance in a single domain achieved considerable progress over the last years. Results are typically validated through experimental evaluation or demonstrated live, e.g., at robotics competitions. While common robot hardware, simulation and programming platforms yield an improved basis, many of the described experiments still cannot be reproduced easily by interested researchers to confirm the reported findings. We consider this a critical challenge for experimental robotics. Hence, we address this problem with a novel process which facilitates the reproduction of robotics experiments. We identify major obstacles to experiment replication and introduce an integrated approach that allows (i) aggregation and discovery of required research artifacts, (ii) automated software build and deployment, as well as (iii) experiment description, repeatable execution and evaluation.We explain the usage of the introduced process along an exemplary robotics experiment and discuss our approach in the context of current ecosystems for robot programming and simulation.

A multi-level control architecture for the bionic handling assistant

The bionic handling assistant is one of the largest soft continuum robots and very special in being a pneumatically operated platform that is able to bend, stretch, and grasp in all directions. It nevertheless shares many challenges with smaller continuum and other soft robots such as parallel actuation, complex movement dynamics, slow pneumatic actuation, non-stationary behavior, and a lack of analytic models. To master the control of this challenging robot, we argue for a tight integration of standard analytic tools, simulation, control, and state-of-the-art machine learning into an overall architecture that can serve as blueprint for control design also beyond the BHA. To this aim, we show how to integrate specific modes of operation and different levels of control in a synergistic manner, which is enabled by using modern paradigms of software architecture and middleware. We thereby achieve an architecture with unique overall control abilities for a soft continuum robot that allow for flexible experimentation toward compliant user-interaction, grasping, and online learning of internal models. Graphical Abstract

Assisted Gravity Compensation to cope with the complexity of kinesthetic teaching on redundant robots

Facilitating efficient programming-by-demonstration methods for advanced robot systems is an ongoing research challenge. This paper addresses one important challenge in this area, which is the programming of kinematically redundant robots. We argue that standard programming-by-demonstration methods for teaching task-space trajectories on a redundant robot using physical human-robot interaction are too complex for non-expert human tutors. We therefore introduce a new interaction and control concept for redundant robot systems, Assisted Gravity Compensation, based on a hierarchical control scheme, separating task-space programming from the redundancy resolution. The user is actively assisted by a given redundancy resolution while kinesthetically teaching task-space trajectories. This control scheme is implemented on our experimental robot system called FlexIRob and we briefly present results of a kinesthetic teaching experiment obtained in a larger field study on physical Human-Robot Interaction with 48 industrial workers. These results show, that the Assisted Gravity Compensation reduces the complexity of a kinesthetic teaching task, which is revealed by an improved task performance, making kinesthetic teaching an efficient programming-by-demonstration method for redundant robots.

A meta-model and toolchain for improved interoperability of robotic frameworks

The emerging availability of high-quality software repositories for robotics promises to speed up the construction process of robotic systems through systematic reuse of software components. However, to reuse components without modification, compatibility at the interface level needs to be created, which is particularly hard if components were implemented in different robotic frameworks. In this paper we propose an approach using model-based techniques for improving component reusability. We specifically address data type compatibility in a structured way through the development of a generic meta-model capable of representing data types from different frameworks and their relations. Based on this model a code generator emits serialization code which makes it possible to seamlessly reuse the existing data types of different frameworks. The application of this approach is exemplified by connecting the YARP-based iCub simulation with a component architecture using a current robotics middleware. Based on our experiences we describe requirements on robotics frameworks to further increase the level of interoperability between available components.

Oncilla Robot: A Versatile Open-Source Quadruped Research Robot With Compliant Pantograph Legs

We present Oncilla robot, a novel mobile, quadruped legged locomotion machine. This large-cat sized, 5.1 kg robot is one of a kind of a recent, bioinspired legged robot class designed with the capability of model-free locomotion control. Animal legged locomotion in rough terrain is clearly shaped by sensor feedback systems. Results with Oncilla robot show that agile and versatile locomotion is possible without sensory signals to some extend, and tracking becomes robust when feedback control is added (Ajallooeian, 2015). By incorporating mechanical and control blueprints inspired from animals, and by observing the resulting robot locomotion characteristics, we aim to understand the contribution of individual components. Legged robots have a wide mechanical and control design parameter space, and a unique potential as research tools to investigate principles of biomechanics and legged locomotion control. But the hardware and controller design can be a steep initial hurdle for academic research. To facilitate the easy start and development of legged robots, Oncilla-robots blueprints are available through open-source. The robots locomotion capabilities are shown in several scenarios. Specifically, its spring-loaded pantographic leg design compensates for overdetermined body and leg postures, i.e., during turning maneuvers, locomotion outdoors, or while going up and down slopes. The robots active degree of freedom allow tight and swift direction changes, and turns on the spot. Presented hardware experiments are conducted in an open-loop manner, with little control and computational effort. For more versatile locomotion control, Oncilla-robot can sense leg joint rotations, and leg-trunk forces. Additional sensors can be included for feedback control with an open communication protocol interface. The robots customized actuators are designed for robust actuation, and efficient locomotion. It trots with a cost of transport of 3.2 J/(Nm), at a speed of 0.63 m s-1 (Froude number 0.25). The robot trots inclined slopes up to 10°, at 0.25 m s-1. The multi-body Webots model of Oncilla robot, and Oncilla robots extensive software architecture enables users to design and test scenarios in simulation. Controllers can directly be transferred to the real robot. Oncilla robots blueprints are open-source published (hardware GLP v3, software LGPL v3).

Domain-Specific Language Modularization Scheme Applied to a Multi-Arm Robotics Use-Case

The development of robotics systems requires a coherent design, implementation, and integration of multiple domainspecific software artifacts that provide the application-specific capabilities. Model-driven software development (MDSD) provides an efficient methodology that enables the design, integration, and verification of robotics applications already at the level of multiple domain-specific models. While the application of MDSD for the engineering of robotics systems is conceptually promising, the interoperability, composability, and reusability of developed domain-specific languages and resulting models are challenging. In this article, we discuss the requirements for language modularization and composition from a robotics perspective and introduce a language composition approach for component-based robotics systems. We use a state-of-the-art language workbench, which supports reuse, extensibility, and refinement of domain-specific languages and code generators. We present and discuss a case study to evaluate the proposed extension and composition approach from a language developer’s perspective as well as from a language user’s perspective, i.e. the perspective of the roboticist supported by our set of domain-specific languages.