2000 character limit reached
Synthetic Dataset Generation and Learning From Demonstration Applied to Industrial Manipulation
Published 30 Mar 2024 in cs.RO | (2404.00447v1)
Abstract: The aim of this study is to investigate an automated industrial manipulation pipeline, where assembly tasks can be flexibly adapted to production without the need for a robotic expert, both for the vision system and the robot program. The objective of this study is first, to develop a synthetic-dataset-generation pipeline with a special focus on industrial parts, and second, to use Learning-from-Demonstration (LfD) methods to replace manual robot programming, so that a non-robotic expert/process engineer can introduce a new manipulation task by teaching it to the robot.
- M. Denninger, M. Sundermeyer, D. Winkelbauer, Y. Zidan, D. Olefir, M. Elbadrawy, A. Lodhi, and H. Katam, “Blenderproc,” arXiv preprint arXiv:1911.01911, 2019.
- H. Ravichandar, A. S. Polydoros, S. Chernova, and A. Billard, “Recent advances in robot learning from demonstration,” Annual review of control, robotics, and autonomous systems, vol. 3, pp. 297–330, 2020.
- S. Peng, Y. Liu, Q. Huang, X. Zhou, and H. Bao, “Pvnet: Pixel-wise voting network for 6dof pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4561–4570.
- A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal, “Dynamical movement primitives: learning attractor models for motor behaviors,” Neural computation, vol. 25, no. 2, pp. 328–373, 2013.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.