Transfer Learning for Machine Learning-based Detection and Separation of Entanglements in Bin-Picking Applications

Moosmann, Marius; Spenrath, Felix; Rosport, Johannes; Melzer, Philipp; Kraus, Werner; Bormann, Richard; Huber, Marco F.

doi:10.1109/iros47612.2022.9981082

Cited by 9 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these approaches use partial visual observation or simple *Correspond to: chou@hlab.sys.es.osaka-u.ac.jp geometrical features such as edges, making it challenging to be adopted in dense clutter. Other studies use pose estimation to evaluate the entanglement level for each object [13], [14]. Such a paradigm relies on the full knowledge of the objects and may suffer from cumulative perception errors due to heavy occlusion or self-occlusion of an individual complexshaped object.…”

Section: Introductionmentioning

confidence: 99%

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Zhang

Domae

Wan

et al. 2023

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Industrial bin picking for tangled-prone objects requires the robot to either pick up untangled objects or perform separation manipulation when the bin contains no isolated objects. The robot must be able to flexibly perform appropriate actions based on the current observation. It is challenging due to high occlusion in the clutter, elusive entanglement phenomena, and the need for skilled manipulation planning. In this paper, we propose an autonomous, effective and general approach for picking up tangled-prone objects for industrial bin picking. First, we learn PickNet -a network that maps the visual observation to pixel-wise possibilities of picking isolated objects or separating tangled objects and infers the corresponding grasp. Then, we propose two effective separation strategies: Dropping the entangled objects into a buffer bin to reduce the degree of entanglement; Pulling to separate the entangled objects in the buffer bin planned by PullNet -a network that predicts position and direction for pulling from visual input. To efficiently collect data for training PickNet and PullNet, we embrace the self-supervised learning paradigm using an algorithmic supervisor in a physics simulator. Realworld experiments show that our policy can dexterously pick up tangled-prone objects with success rates of 90%. We further demonstrate the generalization of our policy by picking a set of unseen objects. Supplementary material, code, and videos can be found at https://xinyiz0931.github.io/tangle.

show abstract

Section: Introductionmentioning

confidence: 99%

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Zhang

Domae

Wan

et al. 2023

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Industrial Bin Picking of Potential Entangled Objects in Dense Clutter by Skeletonized Shape Restoration

Takasu,

Zhang,

Domae

et al. 2024

Springer Proceedings in Advanced Robotics

View full text Add to dashboard Cite

Transfer Learning for Machine Learning-based Detection and Separation of Entanglements in Bin-Picking Applications

Cited by 9 publications

References 20 publications

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

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

Industrial Bin Picking of Potential Entangled Objects in Dense Clutter by Skeletonized Shape Restoration

Contact Info

Product

Resources

About