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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Posts

Future Blog Post

less than 1 minute read

Published: January 01, 2199

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published: August 14, 2015

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published: August 14, 2014

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published: August 14, 2013

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published: August 14, 2012

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

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publications

MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis

Tl;dr: Proposes the first version of MetaGraspNet dataset for robotic picking. Best Paper Award Finalist.

M. Gilles, Y. Chen, T. Robin Winter, E. Zhixuan Zeng and A. Wong, "MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis," 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), Mexico-City, Mexico, August 2022, pp. 220-227, doi: 10.1109/CASE49997.2022.9926427.
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MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy

Tl;dr: Proposes a reliable object detection and segmentation system with Multi Modal Redundancy (MMRNet) for tackling object detection and segmentation.

Y. Chen, H. Gunraj, E. Z. Zeng, R. Meyer, M. Gilles, A. Wong, "MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy ," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2023, pp. 68-77.
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MetaGraspNetV2: All-in-One Dataset Enabling Fast and Reliable Robotic Bin Picking via Object Relationship Reasoning and Dexterous Grasping

Tl;dr: Contributes the second version of MetaGraspNet dataset for robotic picking by adding amodal panoptic segmentation masks and proposing an occlusion-aware fast picking method.

M. Gilles et al., "MetaGraspNetV2: All-in-One Dataset Enabling Fast and Reliable Robotic Bin Picking via Object Relationship Reasoning and Dexterous Grasping," IEEE Transactions on Automation Science and Engineering (T-ASE), 21(3), pp. 2302-2320, July 2024, doi: 10.1109/TASE.2023.3328964.
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MetaMVUC: Active Learning for Sample-Efficient Sim-to-Real Domain Adaptation in Robotic Grasping

Tl;dr: Proposes an active learning framework using an uncertainty- and diversity-based query function for sample-efficient sim-to-real domain adaptation of picking robots.

M. Gilles, K. Furmans and R. Rayyes, "MetaMVUC: Active Learning for Sample-Efficient Sim-to-Real Domain Adaptation in Robotic Grasping,&quot IEEE Robotics and Automation Letters, 10(4), pp.3644-3651, April 2025, doi: 10.1109/LRA.2025.3544083.
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vMF-Contact: Uncertainty-Aware Evidential Learning for Probabilistic Contact-Grasp in Noisy Clutter

Tl;dr: Proposes an uncertainty-aware approach for 6-DoF grasp detection using evidential learning.

Y. Shi, E. Welte, M. Gilles and R. Rayyes, "vMF-Contact: Uncertainty-Aware Evidential Learning for Probabilistic Contact-Grasp in Noisy Clutter," 2025 IEEE International Conference on Robotics and Automation (ICRA), May 2025, pp. 11668-11674, doi: 10.1109/ICRA55743.2025.11127888.
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