Using Deep Neural Networks to Separate Entangled Workpieces in Random Bin Picking

Moosmann, Marius; Spenrath, Felix; Mönnig, Manuel; Khalid, Muhammad Usman; Jaumann, Marvin; Rosport, Johannes; Bormann, Richard

doi:10.1007/978-3-662-62962-8_28

Cited by 9 publications

(1 citation statement)

References 9 publications

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“…Similarly, Moosmann et al [3] trained a CNN to predict whether an object is free from entanglement during a straight lifting-up motion and avoid grasping entangled objects [3]. The same team further developed supervised [16] and reinforcement learning [4] approaches to manipulating and separating entangled objects given task-agnostic grasp poses. Another recent work proposed a topological solution to compute an entanglement score from a depth image and thus find top-down grasp poses that are free from entanglement [6].…”

Section: Related Workmentioning

confidence: 99%

Gam: General Affordance-Based Manipulation for Contact-Rich Object Disentangling Tasks

Yang,

Wu,

Lai

et al. 2023

Preprint

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Section: Related Workmentioning

confidence: 99%

Gam: General Affordance-Based Manipulation for Contact-Rich Object Disentangling Tasks

Yang,

Wu,

Lai

et al. 2023

Preprint

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Using Meta-Learning to Reduce the Effort of Training New Workpiece Geometries for Entanglement Detection in Bin-Picking Applications

Moosmann,

Bleifuß,

Rosport

et al. 2023

Arena2036

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In this paper, we introduce a scaling method for the training of neural networks used for entanglement detection in Bin-Picking. In the Bin-Picking process of complex-shaped and chaotically stored objects, entangled workpieces are a common source of problems. It has been shown that deep neural networks, which are trained using supervised learning, can be used to detect entangled workpieces. However, this strategy requires time-consuming data generation and an additional training process when adapting to previously unseen geometries. To solve this problem, we analyze and compare several Meta-Learning techniques like Reptile, MAML and TAMS for their feasibility as a scaling method for the entanglement detection. These methods search for a strongly generalized model for entanglement detection by learning from the training process of various workpieces with different geometries. Using this generalized model for entanglement detection as initialization helps to increase the learning success with only few training epochs and reduces the required amount of data and therefore the setup effort significantly.

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Performance Comparison of Supervised and Reinforcement Learning Approaches for Separating Entanglements in a Bin-Picking Application

et al. 2023

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Machine Learning helps to separate entanglements in Bin-Picking Applications. The goal is to create a system that finds a path to separate an entanglement, starting from a single visual input. To realize such a system both supervised and reinforcement learning methods can be implemented. For both of these approaches we set up a motion model and the remaining properties of the real robot cell are implemented in a simulation scene. While the simulation scene can be used to create training data for the supervised learning approach, it is also the learning environment for the reinforcement learning model. Therefore, there are similar premises for comparing the two models. What needs to be investigated is which of the two methods separates the most entanglements and offers the least setup effort. The setup effort in general and the performance are examined for both approaches in simulation and also in real-world experiments. The reinforcement learning model outperforms both of the supervised learning models in the setup effort and the separation rate by over 15 percent points.

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Using Deep Neural Networks to Separate Entangled Workpieces in Random Bin Picking

Cited by 9 publications

References 9 publications

Gam: General Affordance-Based Manipulation for Contact-Rich Object Disentangling Tasks

Gam: General Affordance-Based Manipulation for Contact-Rich Object Disentangling Tasks

Using Meta-Learning to Reduce the Effort of Training New Workpiece Geometries for Entanglement Detection in Bin-Picking Applications

Performance Comparison of Supervised and Reinforcement Learning Approaches for Separating Entanglements in a Bin-Picking Application

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