Execution monitoring in robotics: A survey

Pettersson, Ola

doi:10.1016/j.robot.2005.09.004

Cited by 146 publications

(96 citation statements)

References 62 publications

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“…The problem of execution monitoring, well established in fields like industrial control, has gained increased interest in the robotics community [3], since robots need to be robust to failure in uncertain domains. An expectation-based monitor is one that monitors execution by comparing modelgenerated expectations of the world against corresponding observations received during execution.…”

Section: Related Workmentioning

confidence: 99%

Plan execution monitoring through detection of unmet expectations about action outcomes

Mendoza

Veloso

Simmons

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Modeling the effects of actions based on the state of the world enables robots to make intelligent decisions in different situations. However, it is often infeasible to have globally accurate models. Task performance is often hindered by discrepancies between models and the real world, since the true outcome of executing a plan may be significantly worse than the expected outcome used during planning. Furthermore, expectations about the world are often stochastic in robotics, making the discovery of model-world discrepancies non-trivial. We present an execution monitoring framework capable of finding statistically significant discrepancies, determining the situations in which they occur, and making simple corrections to the world model to improve performance. In our approach, plans are initially based on a model of the world that is only as faithful as computational and algorithmic limitations allow. Through experience, the monitor discovers previously unmodeled modes of the world, defined as regions of a feature space in which the experienced outcome of a plan deviates significantly from the predicted outcome. The monitor may then make suggestions to change the model to match the real world more accurately. We demonstrate this approach on the adversarial domain of robot soccer: we monitor pass interception performance of potentially unknown opponents to try to find unforeseen modes of behavior that affect their interception performance.

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Section: Related Workmentioning

confidence: 99%

Plan execution monitoring through detection of unmet expectations about action outcomes

Mendoza

Veloso

Simmons

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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“…The adaptive motion primitives concept introduced in [11] allows the combination of predefined multi-dimensional actions with the primitives that are generated on the fly via analytical solvers during plan graph construction and search processes. Planning efficiency is improved by initially planning for only 4 out of 7 degrees of freedom (DoF) of the manipulator, and then switching to full 7 DoF planning towards the end.…”

Section: Related Workmentioning

confidence: 99%

Improving Prehensile Mobile Manipulation Performance through Experience Reuse

Meriçli

Veloso

Akın

2015

International Journal of Advanced Robotic Systems

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During pick and place tasks, a mobile manipulator performs recurring relative moves within the close proximities of the object of interest and the destination independent of their global poses. These moves are usually critical to the success of the manipulation attempt and hence need to be executed delicately. Considering the critical yet recurring nature of these moves, we let the robot memorize them as state-action sequences and reuse them whenever possible to guide manipulation planning and execution. When combined with a sampling-based generative planner, this guidance helps reduce planning time by deliberately biasing the planning process towards the feasible sequences. Additionally, monitoring the execution while reiterating the reached sequences improves the task success rate. Our experiments show that this complementary combination of the already available partial plans and executions with those generated from scratch yields fast, reliable, and repeatable solutions.

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“…the Tennessee Eastman Process [2] for industrial FDI, do not exist (cf. Pettersson [11]). While erroneous behaviors are frequently observed when operating robots, gathering usable data from these executions is often impossible.…”

Section: Introductionmentioning

confidence: 99%

A Data Set for Fault Detection Research on Component-Based Robotic Systems

Wienke

Borgsen

Wrede

2016

Towards Autonomous Robotic Systems

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Abstract. Fault detection and identification methods (FDI) are an important aspect for ensuring consistent behavior of technical systems. In robotics FDI promises to improve the autonomy and robustness. Existing FDI research in robotics mostly focused on faults in specific areas, like sensor faults. While there is FDI research also on the overarching software system, common data sets to benchmark such solutions do not exist. In this paper we present a data set for FDI research on robot software systems to bridge this gap. We have recorded an HRI scenario with our RoboCup@Home platform and induced diverse empirically grounded faults using a novel, structured method. The recordings include the complete event-based communication of the system as well as detailed performance counters for all system components and exact ground-truth information on the induced faults. The resulting data set is a challenging benchmark for FDI research in robotics which is publicly available.

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Execution monitoring in robotics: A survey

Cited by 146 publications

References 62 publications

Plan execution monitoring through detection of unmet expectations about action outcomes

Plan execution monitoring through detection of unmet expectations about action outcomes

Improving Prehensile Mobile Manipulation Performance through Experience Reuse

A Data Set for Fault Detection Research on Component-Based Robotic Systems

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