Abstract-We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The framework features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state/transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning/prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper.

Abstract. We present a formal framework where a nonmonotonic formalism (the action description language C+) is used to provide robots with high-level reasoning, such as planning, in the style of cognitive robotics. In particular, we introduce a novel method that bridges the high-level discrete action planning and the lowlevel continuous behavior by trajectory planning. We show the applicability of this framework on two LEGO MINDSTORMS NXT robots, in an action domain that involves concurrent execution of actions that cannot be serialized.

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Özetçe -Hanoi kulesi bulmacası, 2011'de AB Robotik koordinasyon hareketinin bir parçası olarak, 2012'de ise IEEE IROS Konferansı'nda bir robotik kabiliyet sınama ortamı olarak yer edinmiştir. Bu ortam, robotların manipülasyon ve algılama yeteneklerinin yüksek seviyede akıl yürütme kabiliyetlerine entegrasyonunun test edilebildigi ortak bir platform saglamıştır. Bu bildiride Hanoi Kulesi bulmacasını genel bir planlama ve icra takibi sistemi içerisinde hedef alıyoruz: bu bulmacayı mantık temelli biçimselcilikle formüle ediyor, ayrık eylem planlama ve sürekli hareket planlamayı birleştiriyor, bu melez planlama problemini güncel otomatik akıl yürütücüleri (SAT çözücüler gibi) kullanarak çözüyor, hesaplanan planları geribeslemeli denetleyiciler kullanarak yürütürken plan başarısızlıklarının telafisi için hataları teşhis edip uygun iyileştirmeleri yapıyoruz. Sundugumuz planlama ve icra takibi sisteminin uygulanılabirligini bir deney düzenegiyle iki robotik manipülatör kullanarak gösteriyoruz.Anahtar Kelimeler-Yapay Zeka Planlama, Manipülasyon Planlama, Sürekli Uzayda Hareket Planlama, Melez Planlama.Abstract -The Tower of Hanoi puzzle, has recently been established as a robotics challenge as a part of EU Robotics coordination action in 2011 and IEEE IROS Conference in 2012. It provides a good standardized test bed to evaluate integration of high-level reasoning capabilities of robots together with their manipulation and perception aspects. We address this challenge within a general planning and monitoring framework: we represent the puzzle in a logic-based formalism, integrate task planning and motion planning, solve this hybrid planning problem with a state-of-the-art automated reasoner (e.g., a SAT solver), execute the computed plans under feedback control while also monitoring for failures, and recover from failures as required. We show the applicability of this framework by implementing it using two robotic manipulators on a physical experimental setup.

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