Object detection using deep learning, CNNs and vision transformers: A review

AB Amjoud, M Amrouch - IEEE Access, 2023 - ieeexplore.ieee.org
Detecting objects remains one of computer vision and image understanding applications'
most fundamental and challenging aspects. Significant advances in object detection have …

Deep learning approaches to grasp synthesis: A review

R Newbury, M Gu, L Chumbley… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Gras** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

Class-incremental learning by knowledge distillation with adaptive feature consolidation

M Kang, J Park, B Han - … of the IEEE/CVF conference on …, 2022 - openaccess.thecvf.com
We present a novel class incremental learning approach based on deep neural networks,
which continually learns new tasks with limited memory for storing examples in the previous …

Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes

M Sundermeyer, A Mousavian… - … on Robotics and …, 2021 - ieeexplore.ieee.org
Gras** unseen objects in unconstrained, cluttered environments is an essential skill for
autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning …

Smart industrial robot control trends, challenges and opportunities within manufacturing

J Arents, M Greitans - Applied Sciences, 2022 - mdpi.com
Industrial robots and associated control methods are continuously develo**. With the
recent progress in the field of artificial intelligence, new perspectives in industrial robot …

Baku: An efficient transformer for multi-task policy learning

S Haldar, Z Peng, L Pinto - Advances in Neural Information …, 2025 - proceedings.neurips.cc
Training generalist agents capable of solving diverse tasks is challenging, often requiring
large datasets of expert demonstrations. This is particularly problematic in robotics, where …