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Conference Papers Year : 2023

PickSim: A dynamically configurable Gazebo pipeline for robotic manipulation

Abstract

State-of-the art robot learning approaches are data-driven and require a large amount of diverse robot data which are prohibitively expensive to acquire. In this paper, we present PickSim, an open source dynamically configurable Gazebo-based pipeline for the purpose of robot manipulation learning. Specifically, we propose a new plugin and pipeline, based on the well known open source robot software Gazebo. We showcase the potential of PickSim through the generation of 6D pose estimation dataset, potentially randomized over a number of factors (e.g., lighting, texture, shape, etc.). Rich and perfect annotations (e.g., object masks, poses, occlusions, etc.) are automatically generated and enable the learning and evaluation of computer vision models over a number of varying factors. PickSim unlocks multi-task robot manipulation learning when a robot model and physics engine are integrated into it, and thereby paves the way to the generation of large-scale robot manipulation data as required in general-purpose robot manipulation learning.
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Dates and versions

hal-04074800 , version 1 (03-05-2023)

Identifiers

  • HAL Id : hal-04074800 , version 1

Cite

Guillaume Duret, Nicolas Cazin, Mahmoud Ali, Florence Zara, Emmanuel Dellandréa, et al.. PickSim: A dynamically configurable Gazebo pipeline for robotic manipulation. Advancing Robot Manipulation Through Open-Source Ecosystems - 2023 IEEE International Conference on Robotics and Automation (ICRA) Conference Workshop, Adam Norton, University of Massachusetts Lowell; Holly Yanco, University of Massachusetts Lowell; Berk Calli, Worcester Polytechnic Institute; Aaron Dollar, Yale University, May 2023, Londres, United Kingdom. ⟨hal-04074800⟩
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