Motion Generators Combined with Behavior Trees: A Novel Approach to Skill Modelling

Rovida, Francesco; Wuthier, David; Großmann, Bjarne; Fumagalli, Matteo; Krüger, Volker

doi:10.1109/iros.2018.8594319

Cited by 32 publications

(52 citation statements)

References 9 publications

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“…The problem of convergence analysis of BTs has been addressed in a number of recent papers, [4], [5], [10], [11]. In general, all of them analyze a linear chain of actions, each satisfying the preconditions of the following.…”

Section: Related Workmentioning

confidence: 99%

“…An analysis of convergence conditions was also made in [4], [5], with the objective of allowing the user to specify the high level goals, then invoking a HTN planner to optimize the execution, including looking for opportunities of parallell execution, of the tasks to be done. An extended version of BTs (eBTs) was defined to bridge the gap between BTs and HTNplanning.…”

Section: Related Workmentioning

confidence: 99%

“…An extended version of BTs (eBTs) was defined to bridge the gap between BTs and HTNplanning. The work presented here goes beyond the work in [4], [5] in that it uses a continuous state formalism, instead of one where the state is composed of a set of conditions that are either true or false.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Convergence Analysis of Hybrid Control Systems in the Form of Backward Chained Behavior Trees

Ögren

2020

IEEE Robot. Autom. Lett.

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A robot control system is often composed of a set of low level continuous controllers and a switching policy that decides which of those continuous controllers to apply at each time instant. The switching policy can be either a Finite State Machine (FSM), a Behavior Tree (BT) or some other structure. In previous work we have shown how to create BTs using a backward chained approach that results in a reactive goal directed policy. This policy can be thought of as providing disturbance rejection at the task level in the sense that if a disturbance changes the state in such a way that the currently running continuous controller cannot handle it, the policy will switch to the appropriate continuous controller. In this letter we show how to provide convergence guarantees for such policies.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Convergence Analysis of Hybrid Control Systems in the Form of Backward Chained Behavior Trees

Ögren

2020

IEEE Robot. Autom. Lett.

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“…A recent work [14] proposed BTs for executing actions in parallel, even when they lie on the same state space (e.g. they use the same robot arm).…”

Section: Related Workmentioning

confidence: 99%

Analysis and Exploitation of Synchronized Parallel Executions in Behavior Trees

Colledanchise

Natale

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Behavior Trees (BTs) are becoming a popular tool to model the behaviors of autonomous agents in the computer game and the robotics industry. One of the key advantages of BTs lies in their composability, where complex behaviors can be built by composing simpler ones. The parallel composition is the one with the highest potential since the complexity of composing pre-existing behaviors in parallel is much lower than the one needed using classical control architectures as finite state machines. However, the parallel composition is rarely used due to the underlying concurrency problems that are similar to the ones faced in concurrent programming.In this paper, we define two synchronization techniques to tackle the concurrency problems in BTs compositions and we show how to exploit them to improve behavior predictability. Also, we introduce measures to assess execution performance, and we show how the design choices can affect them.To illustrate the proposed framework, we provide a set of experiments using the R1 robot and we gather statisticallysignificant data.

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“…Note that the accuracy of the points on the visible surface is usually even higher. We can easily handle this magnitude of errors during assembly using the compliance of our IIWA Kuka arms [14].…”

Section: A Baselinementioning

confidence: 99%

Continuous close-range 3D object pose estimation

Großmann

Rovida

Krüger

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In the context of future manufacturing lines, removing fixtures will be a fundamental step to increase the flexibility of autonomous systems in assembly and logistic operations. Vision-based 3D pose estimation is a necessity to accurately handle objects that might not be placed at fixed positions during the robot task execution. Industrial tasks bring multiple challenges for the robust pose estimation of objects such as difficult object properties, tight cycle times and constraints on camera views. In particular, when interacting with objects, we have to work with close-range partial views of objects that pose a new challenge for typical view-based pose estimation methods. In this paper, we present a 3D pose estimation method based on a gradient-ascend particle filter that integrates new observations on-the-fly to improve the pose estimate. Thereby, we can apply this method online during task execution to save valuable cycle time. In contrast to other view-based pose estimation methods, we model potential views in full 6dimensional space that allows us to cope with close-range partial objects views. We demonstrate the approach on a real assembly task, in which the algorithm usually converges to the correct pose within 10-15 iterations with an average accuracy of less than 8mm.

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Motion Generators Combined with Behavior Trees: A Novel Approach to Skill Modelling

Cited by 32 publications

References 9 publications

Convergence Analysis of Hybrid Control Systems in the Form of Backward Chained Behavior Trees

Convergence Analysis of Hybrid Control Systems in the Form of Backward Chained Behavior Trees

Analysis and Exploitation of Synchronized Parallel Executions in Behavior Trees

Continuous close-range 3D object pose estimation

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