Narrow passage sampling in the observation of robotic assembly tasks

Nottensteiner, Korbinian; Sagardia, Mikel; Stemmer, Andreas; Borst, Christoph

doi:10.1109/icra.2016.7487125

Cited by 19 publications

(16 citation statements)

References 18 publications

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“…In contrast, we present a general approach that combines visual and tactile sensing and continuously tracks the parts in an integrated framework. Therefore, we extend our previous works [54,57] based on intrinsic tactile sensing with an adaptive motion generation component and combine both in an adaptive assembly skill. We provide a brief overview of the system in Section 3, and present the details of the approaches for state estimation in Section 4 and motion generation in Section 5.…”

Section: Introductionmentioning

confidence: 89%

“…Meeussen et al [47,48] implement a particle filter for contact state detection and show how to use it for estimating geometric uncertainties and executing compliant motions. Multiple works estimate geometric uncertainties with particle filters and force measurements in peg-in-hole assembly [4,15,54,65,68], but all of them consider a fixed and rigid hole pose during the assembly. In this work, we will extend our previous works in this field [54,57] for moving parts and suggest an adaptive motion generation procedure for the execution of assembly skills.…”

Section: Bayesian State Estimationmentioning

confidence: 99%

“…A virtual contact model is required for the sample propagation and update in the state estimation. As in our previous works [54,57], we use a fast and accurate penalty-based collision detection algorithm [58] for the contact force and distance computation. The implementation is based on the voxelmap-pointshell (VPS) algorithm by McNeely et al [46].…”

Section: Virtual Contact Modelmentioning

confidence: 99%

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Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

Nottensteiner

Sachtler

Albu‐Schäffer

2021

J Intell Robot Syst

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Robotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

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Section: Introductionmentioning

confidence: 89%

Section: Bayesian State Estimationmentioning

confidence: 99%

Section: Virtual Contact Modelmentioning

confidence: 99%

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Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

Nottensteiner

Sachtler

Albu‐Schäffer

2021

J Intell Robot Syst

Self Cite

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“…During the task execution, the robot learns to replicate the learned forces and torques rather than positions and orientations from the trajectory. The force information may also be used to speed up the subsequent operations of assembly tasks [8,9,[12][13][14][15]46].…”

Section: Force Estimationmentioning

confidence: 99%

“…Peg-in-hole, that is, a robotic gripper grabs the peg and inserts it in a hole. Peg-in-hole is the most essential and representative assembly task that has been widely researched [5][6][7][8][9][10][11][12][13][14]. • Slide-in-the-groove, that is, a robot inserts a bolt fitting inside a groove and slides the bolt to the desired position where the bolt is to be fixed [15].…”

mentioning

confidence: 99%

Robot Learning from Demonstration in Robotic Assembly: A Survey

Zhu

2018

Robotics

203

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Learning from demonstration (LfD) has been used to help robots to implement manipulation tasks autonomously, in particular, to learn manipulation behaviors from observing the motion executed by human demonstrators. This paper reviews recent research and development in the field of LfD. The main focus is placed on how to demonstrate the example behaviors to the robot in assembly operations, and how to extract the manipulation features for robot learning and generating imitative behaviors. Diverse metrics are analyzed to evaluate the performance of robot imitation learning. Specifically, the application of LfD in robotic assembly is a focal point in this paper.

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