Imitating Human Search Strategies for Assembly

Ehlers, Dennis; Suomalainen, Markku; Lundell, Jens; Kyrki, Ville

doi:10.1109/icra.2019.8793780

Cited by 13 publications

(11 citation statements)

References 15 publications

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“…In view of the pick-and-place processes only one policy is learned for the whole assembly process therein, never-theless, it may fail to work in the small parts assembly tasks since features of the assembly motion vary significantly between the two phases. Another formulation of small parts assembly tasks is peg-in-hole problem [14,16,26] and Reinforcement Learning (RL) based methods are widely applied to learn a decision making policy that maps states to actions through trial-anderror [7,8,12,27]. RL-based approaches typically require the robot to explore the state space.…”

Section: Initial Framementioning

confidence: 99%

A robot learning from demonstration framework for skillful small parts assembly

Yang

Lou

2022

Int J Adv Manuf Technol

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Increasing demand for higher production flexibility and smaller production batch size pushes the development of manufacturing expertise towards robotic solutions with fast setup and reprogram capability. Aiming to facilitate assembly lines with robots, the learning from demonstration (LfD) paradigm has attracted attention. A robot LfD framework designed for skillful small parts assembly applications is developed, which takes position, orientation and wrench demonstration data into consideration while utilizes impedance control to deal with the motion error. In view of constraints in industrial assembly applications, we propose a robot LfD framework where policy learning is carried out with separated assembly demonstration data to avoid potential under-fitting problem. With the proposed assembly policies, reference orientation and wrench trajectories are generated as well as coupled with the position data to boost their generalization and robust performance. Effectiveness of the proposed LfD framework is validated by a printed circuit board assembly experiment with a 7-DOF torque-controlled robot.

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Section: Initial Framementioning

confidence: 99%

A robot learning from demonstration framework for skillful small parts assembly

Yang

Lou

2022

Int J Adv Manuf Technol

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“…Many strategies to solve this problem depend on expensive force and torque sensors [17], [18]. Sensorless strategies [19], [20], [21], on the other hand, rely only on the state of the end-effector and provide a low cost solution. However, most of the sensorless strategies depend either on a predetermined trajectory that the robot end-effector needs to follow [21], or a full modeling of the insertion behavior [19], [20].…”

Section: Insertion Tasksmentioning

confidence: 99%

“…Sensorless strategies [19], [20], [21], on the other hand, rely only on the state of the end-effector and provide a low cost solution. However, most of the sensorless strategies depend either on a predetermined trajectory that the robot end-effector needs to follow [21], or a full modeling of the insertion behavior [19], [20]. In [21], insertion is treated as a 3D (2D position and 1D orientation of the end-effector) trajectory tracking problem, where the reference trajectory for the robot end-effector is generated offline by using a coverage strategy such as ergodic control.…”

Section: Insertion Tasksmentioning

confidence: 99%

“…However, most of the sensorless strategies depend either on a predetermined trajectory that the robot end-effector needs to follow [21], or a full modeling of the insertion behavior [19], [20]. In [21], insertion is treated as a 3D (2D position and 1D orientation of the end-effector) trajectory tracking problem, where the reference trajectory for the robot end-effector is generated offline by using a coverage strategy such as ergodic control. As we will show, this strategy fails for the peg-in-hole insertion task considered in this paper, as a trajectory generated offline is often not possible to track due to obstructions from the surface of the hole and the peg.…”

Section: Insertion Tasksmentioning

confidence: 99%

See 1 more Smart Citation

Ergodic Exploration using Tensor Train: Applications in Insertion Tasks

Shetty¹,

Silvério²,

Calinon³

2021

Preprint

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By generating control policies that create natural search behaviors in autonomous systems, ergodic control provides a principled solution to address tasks that require exploration. A large class of ergodic control algorithms relies on spectral analysis, which suffers from the curse of dimensionality, both in storage and computation. This drawback has prohibited the application of ergodic control in robot manipulation since it often requires exploration in state space with more than 2 dimensions. Indeed, the original ergodic control formulation will typically not allow exploratory behaviors to be generated for a complete 6D end-effector pose. In this paper, we propose a solution for ergodic exploration based on spectral analysis in multidimensional spaces using low-rank tensor approximation techniques. We rely on tensor train decomposition, a recent approach from multilinear algebra for low-rank approximation and efficient computation of multidimensional arrays. The proposed solution is efficient both computationally and storage-wise, hence making it suitable for its online implementation in robotic systems. The approach is applied to a peg-in-hole insertion task using a 7-axis Franka Emika Panda robot, where ergodic exploration allows the task to be achieved without requiring the use of force/torque sensors.

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“…Another method is the so-called learning from demonstration method (LfD), where the human transfers its motor skills to the robot by leading the robot with hands. A complex framework is necessary to recover the important parts of the motion and to execute the task successfully [12,13]. To avoid the need of a human operator, self-learning methods are becoming more popular in the last century.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for robotic assembly of fuel cell turbocharger parts with tight tolerances

Aschersleben

Griemert

Gabriel

et al. 2020

Prod. Eng. Res. Devel.

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The efficiency of a fuel cell is not only dependent on the stack, but also to a large extent on the turbocharger, which is responsible for providing the required airflow. Since the individual components, especially those of the rotor, are subject to high demands on manufacturing accuracy, it is crucial to ensure a precise and robust assembly. In order to achieve a scalable assembly process, this paper presents a method for a robot-based assembly of the rotationally symmetric components of the rotor. The assembly task has been reduced to the two essential problems: search and insertion. On this basis, a system was developed, which is able to learn the joining process independently and compensate for positioning inaccuracies with the help of reinforcement learning in combination with a position-controlled robot. The applied reinforcement learning strategy is based on the measurement data of a 6-axis force/torque sensor, with which the current contact state can be evaluated and a decision for the next step can be made. The experimental verification shows that an automation of the assembly process is possible with the proposed strategy. The robot is able to perform the search operation successfully, whereas limitations to the achievable accuracies of the insertion process could be found.

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Imitating Human Search Strategies for Assembly

Cited by 13 publications

References 15 publications

A robot learning from demonstration framework for skillful small parts assembly

A robot learning from demonstration framework for skillful small parts assembly

Ergodic Exploration using Tensor Train: Applications in Insertion Tasks

Reinforcement learning for robotic assembly of fuel cell turbocharger parts with tight tolerances

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