Proceedings Article•
Planning Domain + Execution Semantics: A Way Towards Robust Execution?
01 Jan 2014-
TL;DR: It is shown that the combined used of causal, temporal and categorical knowledge allows the robot to detect failures even when the effects of actions are not directly observable.
Abstract: Robots are expected to carry out complex plans in real world environments. This requires the robot to track the progress of plan execution and detect failures which may occur. Planners use very abstract world models to generate plans. Additional causal, temporal, categorical knowledge about the execution, which is not included in the planner's model, is often avail- able. Can we use this knowledge to increase robustness of execution and provide early failure detection? We propose to use a dedicated Execution Model to monitor the executed plan based on runtime observations and rich execution knowl- edge. We show that the combined used of causal, temporal and categorical knowledge allows the robot to detect failures even when the effects of actions are not directly observable. A dedicated Execution model also introduces a degree of mod- ularity, since the platform- and execution-specific knowledge does not need to be encoded into the planner.
Citations
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TL;DR: This study elaborate on a framework that allows a humanoid robot to understand natural language, derive symbolic representations of its sensorimotor experience, generate complex plans according to the current world state, and monitor plan execution.
Abstract: We propose an approach for instructing a robot using natural language to solve complex tasks in a dynamic environment. In this study, we elaborate on a framework that allows a humanoid robot to understand natural language, derive symbolic representations of its sensorimotor experience, generate complex plans according to the current world state, and monitor plan execution. The presented development supports replacing missing objects and suggesting possible object locations. It is a realization of the concept of structural bootstrapping developed in the context of the European project Xperience. The framework is implemented within the robot development environment ArmarX. We evaluate the framework on the humanoid robot ARMAR-III in the context of two experiments: a demonstration of the real execution of a complex task in the kitchen environment on ARMAR-III and an experiment with untrained users in a simulation environment.
19 citations
Cites background from "Planning Domain + Execution Semanti..."
...Replacement of missing objects and re-planning is performed in [39, 69]....
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Proceedings Article•
12 Jun 2016TL;DR: The notion of experience-based planning domains for task-level learning and planning in robotics is explored by integrating goal inference capabilities and is illustrated in a restaurant environment where a service robot learns how to carry out complex tasks.
Abstract: Learning and deliberation are required to endow a robot with the capabilities to acquire knowledge, perform a variety of tasks and interactions, and adapt to open-ended environments. This paper explores the notion of experience-based planning domains (EBPDs) for task-level learning and planning in robotics. EBPDs rely on methods for a robot to: (i) obtain robot activity experiences from the robot's performance; (ii) conceptualize each experience to a task model called activity schema; and (iii) exploit the learned activity schemata to make plans in similar situations. Experiences are episodic descriptions of plan-based robot activities including environment perception, sequences of applied actions and achieved tasks. The conceptualization approach integrates different techniques including deductive generalization, abstraction and feature extraction to learn activity schemata. A high-level task planner was developed to find a solution for a similar task by following an activity schema. In this paper, we extend our previous approach by integrating goal inference capabilities. The proposed approach is illustrated in a restaurant environment where a service robot learns how to carry out complex tasks.
14 citations
Cites methods from "Planning Domain + Execution Semanti..."
...The execution manager receives the plan generated by the planner and dispatches the planned actions to the robot platform, and records success or failure information into the working memory (Konec̆ný et al. 2014)....
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17 Dec 2015
TL;DR: An integrated system that uses a physics-based simulation to predict robot action results and durations, combined with a Hierarchical Task Network (HTN) planner and semantic execution monitoring and improves on state-of-the-art AI plan-based systems by feeding simulated prediction results back into the execution system.
Abstract: Real-world robotic systems have to perform reliably in uncertain and dynamic environments. State-of-the-art cognitive robotic systems use an abstract symbolic representation of the real world for high-level reasoning. Some aspects of the world, such as object dynamics, are inherently difficult to capture in an abstract symbolic form, yet they influence whether the executed action will succeed or fail. This paper presents an integrated system that uses a physics-based simulation to predict robot action results and durations, combined with a Hierarchical Task Network (HTN) planner and semantic execution monitoring. We describe a fully integrated system in which a Semantic Execution Monitor (SEM) uses information from the planning domain to perform functional imagination. Based on information obtained from functional imagination, the robot control system decides whether it is necessary to adapt the plan currently being executed. As a proof of concept, we demonstrate a PR2 able to carry tall objects on a tray without the objects toppling. Our approach achieves this by simulating robot and object dynamics. A validation shows that robot action results in simulation can be transferred to the real world. The system improves on state-of-the-art AI plan-based systems by feeding simulated prediction results back into the execution system.
11 citations
Cites background from "Planning Domain + Execution Semanti..."
...It also maintains a temporal network, which represents temporal expectations about execution [17], e....
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TL;DR: The notion of experience-based planning domains (EBPDs) for task level learning and planning in robotics are presented and the proposed approach is illustrated and evaluated in a restaurant environment where a service robot learns how to carry out complex tasks.
Abstract: Deliberation and learning are required to endow a robot with the capabilities for acquiring knowledge, performing a variety of tasks and interactions, and adapting to open-ended environments. This paper presents the notion of experience-based planning domains (EBPDs) for task level learning and planning in robotics. EBPDs provide methods for a robot to: (i) obtain robot activity experiences from the robot's performance in a dynamic environment; (ii) conceptualize each experience producing an activity schema; and (iii) exploit the learned activity schemata to make plans in similar situations. Experiences are episodic descriptions of plan-based robot activities including environment perception, sequences of applied actions and achieved tasks. The conceptualization approach integrates different techniques including deductive generalization, abstraction, goal inference and feature extraction. A high-level task planner was developed to find a solution for a task by following an activity schema. The proposed approach is illustrated and evaluated in a restaurant environment where a service robot learns how to carry out complex tasks.
11 citations
Proceedings Article•
01 Jan 2018TL;DR: This paper proposes to model the internals of a robot system and its ties to the actions that the robot can perform, and proposes an online transformation of an abstract plan into executable actions conforming with system specifics.
Abstract: In this paper, we are concerned with making the execution of abstract action plans for robotic agents more robust. To this end, we propose to model the internals of a robot system and its ties to the actions that the robot can perform. Based onaction plans for robotic agents more robust. To this end, we propose to model the internals of a robot system and its ties to the actions that the robot can perform. Based on these models, we propose an online transformation of an abstract plan into executable actions conforming with system specifics. With our framework, we aim to achieve two goals. First, modeling the system internals is beneficial in its own right in order to achieve long term autonomy, system transparency, and comprehensibility. Second, separating the system details from determining the course of action on an abstract level leverages the use of planning for actual robotic
5 citations
Cites background from "Planning Domain + Execution Semanti..."
...Konečnỳ et al. (2014) separate the strategic planning layer that only handles an abstract domain conceptualization from the detailed execution strategy that makes a plan executable on a real robot....
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References
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Proceedings Article•
01 Jan 2009TL;DR: This paper discusses how ROS relates to existing robot software frameworks, and briefly overview some of the available application software which uses ROS.
Abstract: This paper gives an overview of ROS, an opensource robot operating system. ROS is not an operating system in the traditional sense of process management and scheduling; rather, it provides a structured communications layer above the host operating systems of a heterogenous compute cluster. In this paper, we discuss how ROS relates to existing robot software frameworks, and briefly overview some of the available application software which uses ROS.
8,387 citations
"Planning Domain + Execution Semanti..." refers background in this paper
...All modules communicate through the ROS (Quigley et al. 2009) middleware....
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...A common way to execute and monitor a plan in ROS is by using a Finate State Machine architecture (Bohren et al. 2011)....
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TL;DR: The procedure was originally programmed in FORTRAN for the Control Data 160 desk-size computer and was limited to te t ra t ion because subroutine recursiveness in CONTROL Data 160 FORTRan has been held down to four levels in the interests of economy.
Abstract: procedure ari thmetic (a, b, c, op); in t eger a, b, c, op; ¢ o n l m e n t This procedure will perform different order ar i thmetic operations with b and c, put t ing the result in a. The order of the operation is given by op. For op = 1 addit ion is performed. For op = 2 multiplicaLion, repeated addition, is done. Beyond these the operations are non-commutat ive. For op = 3 exponentiat ion, repeated multiplication, is done, raising b to the power c. Beyond these the question of grouping is important . The innermost implied parentheses are at the right. The hyper-exponent is always c. For op = 4 te t ra t ion, repeated exponentiat ion, is done. For op = 5, 6, 7, etc., the procedure performs pentat ion, hexation, heptat ion, etc., respectively. The routine was originally programmed in FORTRAN for the Control Data 160 desk-size computer. The original program was limited to te t ra t ion because subroutine recursiveness in Control Data 160 FORTRAN has been held down to four levels in the interests of economy. The input parameter , b, c, and op, must be positive integers, not zero; b e g i n own i n t e g e r d, e, f, drop; i f o p = 1 t h e n b e g i n a := h-4c; go t o l e n d i f o p = 2 t h e n d := 0; else d := 1; e := c; drop := op 1; for f := I s t e p 1 u n t i l e do b e g i n ari thmetic (a, b, d, drop);
3,848 citations
"Planning Domain + Execution Semanti..." refers background in this paper
...These bounds are updated as a result of temporal constraint reasoning, an operation which can be performed in low-order polynomial time (Dechter, Meiri, and Pearl 1991; Floyd 1962)....
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01 Jan 2005
TL;DR: A formalism for reasoning about actions that is based on a temporal logic allows a much wider range of actions to be described than with previous approaches such as the situation calculus and a framework for planning in a dynamic world with external events and multiple agents is suggested.
Abstract: A formalism for reasoning about actions is proposed that is based on a temporal logic. It allows a much wider range of actions to be described than with previous approaches such as the situation calculus. This formalism is then used to characterize the different types of events, processes, actions, and properties that can be described in simple English sentences. In addressing this problem, we consider actions that involve non-activity as well as actions that can only be defined in terms of the beliefs and intentions of the actors. Finally, a framework for planning in a dynamic world with external events and multiple agents is suggested.
2,631 citations
"Planning Domain + Execution Semanti..." refers methods in this paper
...We model temporal relations between fluents as constraints in Allen’s Interval Algebra (IA) (Allen 1984)....
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TL;DR: In this article, a formalism for reasoning about actions is proposed that is based on a temporal logic, which allows a much wider range of actions to be described than with previous approaches such as the situation calculus.
Abstract: A formalism for reasoning about actions is proposed that is based on a temporal logic. It allows a much wider range of actions to be described than with previous approaches such as the situation calculus. This formalism is then used to characterize the different types of events, processes, actions, and properties that can be described in simple English sentences. In addressing this problem, we consider actions that involve non-activity as well as actions that can only be defined in terms of the beliefs and intentions of the actors. Finally, a framework for planning in a dynamic world with external events and multiple agents is suggested.
2,439 citations
TL;DR: It is shown that the STP, which subsumes the major part of Vilain and Kautz's point algebra, can be solved in polynomial time and the applicability of path consistency algorithms as preprocessing of temporal problems is studied, to demonstrate their termination and bound their complexities.
Abstract: This paper extends network-based methods of constraint satisfaction to include continuous variables, thus providing a framework for processing temporal constraints. In this framework, called temporal constraint satisfaction problem (TCSP), variables represent time points and temporal information is represented by a set of unary and binary constraints, each specifying a set of permitted intervals. The unique feature of this framework lies in permitting the processing of metric information, namely, assessments of time differences between events. We present algorithms for performing the following reasoning tasks: finding all feasible times that a given event can occur, finding all possible relationships between two given events, and generating one or more scenarios consistent with the information provided. We distinguish between simple temporal problems (STPs) and general temporal problems, the former admitting at most one interval constraint on any pair of time points. We show that the STP, which subsumes the major part of Vilain and Kautz's point algebra, can be solved in polynomial time. For general TCSPs, we present a decomposition scheme that performs the three reasoning tasks considered, and introduce a variety of techniques for improving its efficiency. We also study the applicability of path consistency algorithms as preprocessing of temporal problems, demonstrate their termination and bound their complexities.
1,989 citations
"Planning Domain + Execution Semanti..." refers background in this paper
...The STP maintains a time bound [se, sl] for the start of ψ consisting of earliest possible start time se and latest possible start time sl....
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...A STP maintains a lower and an upper bound for each time point (start or finish time of a fluent)....
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...Such a network can be transformed into a Simple Temporal Problem — STP (Dechter, Meiri, and Pearl 1991)....
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...These bounds are updated as a result of temporal constraint reasoning, an operation which can be performed in low-order polynomial time (Dechter, Meiri, and Pearl 1991; Floyd 1962)....
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