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Titlebook: Advances in Service and Industrial Robotics; RAAD 2023 Tadej Petri?,Ale? Ude,Leon ?lajpah Conference proceedings 2023 The Editor(s) (if app

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31#
發(fā)表于 2025-3-27 00:35:56 | 只看該作者
Accessing and Using Chemical Databasesons of moving targets by the moving robot. We validate the effectiveness of the method in an experimental setup using a 6-DoF robot arm and an RGB-D camera by detecting and handling occlusions for two tasks: Pose estimation of a moving object for pickup and human tracking for robot handover. The cod
32#
發(fā)表于 2025-3-27 02:34:47 | 只看該作者
33#
發(fā)表于 2025-3-27 06:28:22 | 只看該作者
34#
發(fā)表于 2025-3-27 10:38:43 | 只看該作者
35#
發(fā)表于 2025-3-27 16:29:14 | 只看該作者
Muthukumarasamy Karthikeyan,Renu Vyasplored the application of several deep learning architectures based on two-dimensional Convolutional Neural Networks and Convolutional Long Short-Term Memory Recurrent Neural Networks. Images obtained from the real-world experimental installation were utilized for model design. Our deep learning mod
36#
發(fā)表于 2025-3-27 18:02:09 | 只看該作者
Muthukumarasamy Karthikeyan,Renu Vyasd energy capacity. The joint is quasi-passive, meaning it only uses electricity to power the pump and the fast-switching valves. This implies that the joint can only store and reuse the energy, but not generate the torque. The article gives a system overview with the mathematical model, after which
37#
發(fā)表于 2025-3-27 22:34:17 | 只看該作者
38#
發(fā)表于 2025-3-28 03:50:31 | 只看該作者
39#
發(fā)表于 2025-3-28 08:04:26 | 只看該作者
40#
發(fā)表于 2025-3-28 13:44:14 | 只看該作者
Exploring the?Potential and?Challenges of?Reinforcement Learning for?Object Pushing with?Robotsmework. Using reinforcement learning, we aim to explore different strategies for playing air hockey and non-prehensile object manipulation. To achieve this, we first trained our models in simulation and then transferred the successful ones to a Franka Emika Panda robot for testing on a real system.
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