A deep learning approach for final grasping state determination from motion trajectory of a prosthetic hand - ScienceDirectDeep Learning has been gaining popularity due to its numerous implementations and continuous growing capabilities, including the prosthetics industry which has trend of evaluation ...
Sayour MH, Kozhaya SE, Saab SS (2022) Autonomous robotic manipulation: real-time, deep-learning approach for grasping of unknown objects. J Robot 2585656:14. https://doi.org/10.1155/2022/2585656 Article Google Scholar Saab S Jr, Fu Y, Ray A, Hauser M (2022) A dynamically stabilized ...
2024 arxiv HiFi-CS Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models N/A 2025 ECCV Ferret-UI Grounded Mobile UI Understanding with Multimodal LLMs N/A 4. Datasets and Benchmarks 3.1 The Five Datasets for Classical Visual Grounding YearVenueNamePaper Title ...
A Rapid Adaptation Approach for Dynamic Air-Writing Recognition Using Wearable Wristbands with Self-Supervised Contrastive Learning Yunjian Guo Kunpeng Li Jong-Chul Lee Nano-Micro Letters(2025) Intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmen...
We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to...
J Krause, B Sapp, A Howard, H Zhou, A Toshev, T Duerig et al., The unreasonable effectiveness of noisy data for fine-grained recognition, in Google Scholar S Levine, P Pastor, A Krizhevsky, D Quillen, Learning hand-eye coordination for robotic grasping with deep learning and large-sca...
In motor BCIs, most effort has focused on controlling single effectors such as computer cursors for point-and-click cursor control and robotic arms for reaching and grasping (where fingers moved as a group)8,9,10,11,12,13,14,15,16. To expand object manipulation, ref.17continuously decoded ...
This review provides a comprehensive overview of machine learning approaches for vision-based robotic grasping and manipulation. Current trends and develop
Deep reinforcement learning includes on-policy and off-policy methods. Off-policy methods, use the behavior policy πb for exploration and the target policy π for decision-making. For on-policy methods, the behavior policy is the same as the target policy. Q-learning [8] is an off-policy...
For example, for intelligent robotics, the incomplete point cloud data of 3d objects usually make it difficult to recognize the target object to be grasped correctly and also difficult to perform accurate localization and grasping. In the applications of SLAM and augmented reality, the missing point...