Learning Control

Calinon, Sylvain; Lee, Dongheui

doi:10.1007/978-94-007-6046-2_68

Cited by 35 publications

(35 citation statements)

References 91 publications

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“…In this case, the proposed segmentation mechanism produces three segments for the take-spoon task: the robot has to reach the proximity region of the spoon firstly (a1(spoon), equivalent to a pre-grasping pose), then the grasping pose (a2(spoon)), and to close the gripper. Although this structure, along with the associated pre-and post-conditions, allows to execute the demonstrated trajectories in the right order 1 , it produces a large number of nodes that can be suitably compressed without altering the task execution. This problem is of particular interest in this context, because the attentional system periodically checks the task execution state to command the most emphasized action, and the time needed to compute the most emphasized action grows with the number of nodes in the tree.…”

Section: A Problem Descriptionmentioning

confidence: 99%

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Symbolic Task Compression in Structured Task Learning

Saveriano¹,

Seegerer²,

Caccavale³

et al. 2019

2019 Third IEEE International Conference on Robotic Computing (IRC)

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Learning everyday tasks from human demonstrations requires unsupervised segmentation of seamless demonstrations, which may result in highly fragmented and widely spread symbolic representations. Since the time needed to plan the task depends on the amount of possible behaviors, it is preferable to keep the number of behaviors as low as possible. In this work, we present an approach to simplify the symbolic representation of a learned task which leads to a reduction of the number of possible behaviors. The simplification is achieved by merging sequential behaviors, i.e. behaviors which are logically sequential and act on the same object. Assuming that the task at hand is encoded in a rooted tree, the approach traverses the tree searching for sequential nodes (behaviors) to merge. Using simple rules to assign pre-and post-conditions to each node, our approach significantly reduces the number of nodes, while keeping unaltered the task flexibility and avoiding perceptual aliasing. Experiments on automatically generated and learned tasks show a significant reduction of the planning time.

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Section: A Problem Descriptionmentioning

confidence: 99%

“…In order to simplify robot programming, researchers focused on learning tasks from human demonstrations and contextual information [1]- [9]. These approaches are effective in learning symbolic and robot-independent task representations from observation.…”

Section: Introductionmentioning

confidence: 99%

Symbolic Task Compression in Structured Task Learning

Saveriano¹,

Seegerer²,

Caccavale³

et al. 2019

2019 Third IEEE International Conference on Robotic Computing (IRC)

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“…Examples with a link to statistical learning include representations based on hidden Markov models (HMMs), with many variants such as incremental learning extensions [21], the inclusion of dynamic features to retrieve trajectory distributions [9], the local encoding of state durations to handle partial demonstrations [47], or the exploitation of the hierarchical organization capability of HMMs [20]. Another key challenge closely related to statistical representations in LfD concerns the problem of autonomously segmenting and abstracting the continuous flow of demonstration [40,31,19,23].…”

Section: Key Research Findingsmentioning

confidence: 99%

Learning from Demonstration (Programming by Demonstration)

Calinon¹

2018

Encyclopedia of Robotics

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“…Programming a robot to use exploration motions by conventional interfaces is elaborate and requires expert knowledge. To ease this procedure, learning from demonstration [6] is employed as teaching interface to directly extract the desired behavior. Hereby, the user demonstrates the task 1 German Aerospace Center (DLR), Institute of Robotics and Mechatronics, Wessling, Germany consisting of exploration and manipulation motions multiple times by kinesthetic teaching.…”

Section: Introductionmentioning

confidence: 99%

Learning Haptic Exploration Schemes for Adaptive Task Execution

Eiband

Saveriano

Lee

2019

2019 International Conference on Robotics and Automation (ICRA)

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The recent generation of compliant robots enables kinesthetic teaching of novel skills by human demonstration. This enables strategies to transfer tasks to the robot in a more intuitive way than conventional programming interfaces. Programming physical interactions can be achieved by manually guiding the robot to learn the behavior from the motion and force data. To let the robot react to changes in the environment, force sensing can be used to identify constraints and act accordingly. While autonomous exploration strategies in the whole workspace are time consuming, we propose a way to learn these schemes from human demonstrations in an object targeted manner. The presented teaching strategy and the learning framework allow to generate adaptive robot behaviors relying on the robot's sense of touch in a systematically changing environment. A generated behavior consists of a hierarchical representation of skills, where haptic exploration skills are used to touch the environment with the end effector, and relative manipulation skills, which are parameterized according to previous exploration events. The effectiveness of the approach has been proven in a manipulation task, where the adaptive task structure is able to generalize to unseen object locations. The robot autonomously manipulates objects without relying on visual feedback.

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Learning Control

Cited by 35 publications

References 91 publications

Symbolic Task Compression in Structured Task Learning

Symbolic Task Compression in Structured Task Learning

Learning from Demonstration (Programming by Demonstration)

Learning Haptic Exploration Schemes for Adaptive Task Execution

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