Domain-Independent Disperse and Pick method for Robotic Grasping

Raj, Prem; Singhal, Aniruddha; Sanap, Vipul; Behera, Laxmidhar; Sinha, Rajesh Kumar

doi:10.1109/ijcnn55064.2022.9892672

Cited by 5 publications

References 26 publications

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Towards Object Agnostic and Robust 4-DoF Table-Top Grasping

Raj

Kumar

Sanap³

et al. 2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

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This paper addresses category-agnostic instance segmentation for robotic manipulation, focusing on segmenting objects independent of their class to enable versatile applications like bin-picking in dynamic environments. Existing methods often lack generalizability and object-specific information, leading to grasp failures. We present a novel approach leveraging objectcentric instance segmentation and simulation-based training for effective transfer to real-world scenarios. Notably, our strategy overcomes challenges posed by noisy depth sensors, enhancing the reliability of learning. Our solution accommodates transparent and semi-transparent objects which are historically difficult for depth-based grasping methods. Contributions include domain randomization for successful transfer, our collected dataset for warehouse applications, and an integrated framework for efficient bin-picking. Our trained instance segmentation model achieves state-of-the-art performance over WISDOM public benchmark [1] and also over the custom-created dataset. In a real-world challenging bin-picking setup our bin-picking framework method achieves 98% accuracy for opaque objects and 97% accuracy for non-opaque objects, outperforming the state-of-theart baselines with a greater margin.

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