Symbolic reasoners generate plans which are often not exploiting the robot capabilities and are sensitive to runtime disturbances. This work proposes a scheduler as an interface between a discrete, symbolic plan and a motion control based on constraint optimization. Acting as a local reasoner, the scheduler valuates a set of predicates to decide when an action will be executed. Given a task specification which describes how the action should be realized, the scheduler configures the controller at runtime. A demonstration will be provided considering an “open drawer” scenario.

Combining tasks, melding their activities in sequence and in parallel, in order to achieve the desired goal is a challenging research topic. In many practical applications, tasks could be fulfilled even though the robot does not achieve a perfect matching with a given quantitative objective. Thus, it could be possible to carry out other activities, mildly conflicting with the current goal, yet providing benefits for the overall execution of a complex sequence of tasks. This paper proposes i) a criteria to evaluate the execution of a monitored task, ii) an enriched task specification to express the tolerance with which a goal is fulfilled, so that iii) a sequence of tasks can be executed concurrently, on the basis of monitored quantities. Furthermore, the paper reports a classification of conflicting scenarios for a finer selection of the scheduler policy. Finally, results from experimental scenarios show the potential benefits of the proposed methodology.

Task specification models define the activities to be executed by a robot in order to achieve its goal. Classical examples are the sequences involved in assembly or pick and place tasks. This work introduces the preview coordination execution model, an extension to the traditional way in which the execution of such task specifications is coordinated at runtime. Instead of taking activities one-by-one as defined in the task specification model, preview coordination optimizes the task scheduling based on knowledge about the likelihood that not just the activities required by the current state can be executed, but that also one or more of those related to future states of the system can be activated. An experiment with mobile manipulation tasks illustrates the benefits of preview coordination.

This paper discusses the theoretical background and practical implementation of a large-scale, low-performance haptic remote control setup. The experimental system consists of a pair of KUKA Light Weight Robots (LWR) coupled to a Willow Garage Personal Robot (PR2) via two different robotic frameworks. The haptic “performance” is, of course, not comparable to dedicated haptic applications, but has its use as a test-bed for interaction between “legacy” service robot systems, that have not been especially designed for mutual haptic interaction. We discuss some major application problems, and the future work needed for nonuniform robot coupling. Beside haptic coupling, we provide the human operator with visual feedback. To this end, the head movements of the human operator are coupled to the head movement of the PR2 and the images of the eye cameras are displayed to the human operator using a wearable display. The presented teleoperation application is furthermore an example of the integration of two component-based robotic frameworks namely OROCOS (Open Robot Control Software)and ROS (Robot Operating System) Experimental results regarding the haptic coupling are presented using an “artistic” painting task for qualitative results, and a hard contact at the slave side for quantitative results.