To solve ever more complex and longer tasks, mobile robots need to generate more elaborate plans and must handle dynamic environments and incomplete knowledge. We address this challenge by integrating two seemingly different approaches — PDDL-based planning for efficient plan generation and Golog for highly expressive behavior specification — in a coherent framework that supports continual planning. The latter allows to interleave plan generation and execution through assertions, which are placeholder actions that are dynamically expanded into conditional sub-plans (using classical planners) once a replanning condition is satisfied. We formalize and implement continual planning in Golog which was so far only supported in PDDL-based systems. This enables combining the execution of generated plans with regular Golog programs and execution monitoring. Experiments on autonomous mobile robots show that the approach supports expressive behavior specification combined with efficient sub-plan generation to handle dynamic environments and incomplete knowledge in a unified way.

Mobile robots should perform a growing number of tasks and react to time-critical events. Thus, the ability to interrupt a task and resume it later is crucial. While interleaved execution occurs often in robotics, existing approaches do not consider the fact that interrupting a task and resuming an interrupted task often requires intermediate steps. In this paper we present an approach to interruptible task execution with resumption. We propose INTRGOLOG which extends INDIGOLOG by task interruption and resumption through introducing new constructs to determine and fulfill the requirements of tasks. Our experiments on a service robot and in simulation show that the ability to switch to another task enables a robot to react in a swift and reliable fashion to new events.

Robot sensors are usually subject to error. Since in many practical scenarios a probabilistic error model is not available, sensor readings are often dealt with in a hard-coded, heuristic fashion. In this paper, we propose a logic to address the problem from a KR perspective. In this logic, the epistemic effect of sensing actions is deferred to so-called fusion actions, which may resolve discrepancies and inconsistencies of recent sensing results. Moreover, a local closed-world assumption can be applied dynamically. When needed, this assumption can be revoked and fusions can be undone using a form of forgetting.

The RoboCup Logistics League (RCLL) has seen major rule changes increasing the complexity, e.g. by raising the number of product variants from 3 to almost 250, and introducing new challenges like the handling of physical processing machines. We describe various aspects of our system that allowed to improve the performance in 2015 and our efforts to advance the league as a whole. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Autonomous mobile robots comprise a great deal of complexity. They require a plethora of software components for perception, actuation, task-level reasoning, and communication. These components have to be integrated into a coherent and robust system in time for the next RoboCup event. Then, during the competition, the system has to perform stable and reliably. Providing a software framework for teams to use tremendously eases that effort. Even more so when providing a fully integrated system specific for a particular domain. We have recently released our full software stack for the RoboCup Logistics League (RCLL) based on the Open Source Fawkes Robot Software Framework . This release includes all software components of the RoboCup 2014 winning team Carologistics (in cooperation with the AllemaniACs RoboCup@Home team). It specifically also includes the parts of the software which are domain or platform specific or which we consider our competitive edge and were kept private until now. We think that this will make the league much more accessible to new teams and might help existing teams to improve their performance. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In the RoboCup Logistics League (RCLL), games are governed by a semi-autonomous referee box. It also records tremendous amounts of data about state changes of the game or communication with the robots. In this paper, we analyze the data of the 2014 competition by means of Key Performance Indicators (KPI). KPIs are used in industrial environments to evaluate the performance of production systems. Applying adapted KPIs to the RCLL provides interesting insights about the strategies of the robot teams. When aiming for more realistic industrial properties with a 24/7 production, where teams perform shifts (without intermediate environment reset), KPIs could be a means to score the game. This could be tried first in a simulation sub-league. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

During operation of robots large amounts of data are produced and processed for instance in perception, actuation, or decision making. Nowadays this data is typically volatile and disposed right after use. But this data can be valuable and useful later. Therefore we propose a database system that taps into common robot middleware to record any and all data produced at run-time. We present two examples using this data in fault analysis and performance evaluation and describe real-world experiments run on the domestic service robot HERB.

We present the hardware design, software architecture, and core algorithms of Herb 2.0, a bimanual mobile manipulator developed at the Personal Robotics Lab at Carnegie Mellon University, Pittsburgh, PA. We have developed Herb 2.0 to perform useful tasks for and with people in human environments. We exploit two key paradigms in human environments: that they have structure that a robot can learn, adapt and exploit, and that they demand general-purpose capability in robotic systems. In this paper, we reveal some of the structure present in everyday environments that we have been able to harness for manipulation and interaction, comment on the particular challenges on working in human spaces, and describe some of the lessons we learned from extensively testing our integrated platform in kitchen and office environments.