| 1. |
- Kokic, Mia, 1992-, et al.
(author)
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Learning Task-Oriented Grasping From Human Activity Datasets
- 2020
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In: IEEE Robotics and Automation Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2377-3766. ; 5:2, s. 3352-3359
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Journal article (peer-reviewed)abstract
- We propose to leverage a real-world, human activity RGB dataset to teach a robot Task-Oriented Grasping (TOG). We develop a model that takes as input an RGB image and outputs a hand pose and configuration as well as an object pose and a shape. We follow the insight that jointly estimating hand and object poses increases accuracy compared to estimating these quantities independently of each other. Given the trained model, we process an RGB dataset to automatically obtain the data to train a TOG model. This model takes as input an object point cloud and outputs a suitable region for task-specific grasping. Our ablation study shows that training an object pose predictor with the hand pose information (and vice versa) is better than training without this information. Furthermore, our results on a real-world dataset show the applicability and competitiveness of our method over state-of-the-art. Experiments with a robot demonstrate that our method can allow a robot to preform TOG on novel objects.
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| 2. |
- Newbury, Rhys, et al.
(author)
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Deep Learning Approaches to Grasp Synthesis: A Review
- 2023
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In: IEEE Transactions on robotics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1552-3098 .- 1941-0468. ; 39:5, s. 3994-4015
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Journal article (peer-reviewed)abstract
- Grasping 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 object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all six degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches In addition, we found two 'supporting methods' around grasping that use deep learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research.
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