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pro vyhledávání: '"Singh, Rohan Pratap"'
Recent advances in deep reinforcement learning (RL) based techniques combined with training in simulation have offered a new approach to developing robust controllers for legged robots. However, the application of such approaches to real hardware has
Externí odkaz:
http://arxiv.org/abs/2303.03724
Autor:
Singh, Rohan Pratap, Benallegue, Mehdi, Morisawa, Mitsuharu, Cisneros, Rafael, Kanehiro, Fumio
Deep reinforcement learning (RL) based controllers for legged robots have demonstrated impressive robustness for walking in different environments for several robot platforms. To enable the application of RL policies for humanoid robots in real-world
Externí odkaz:
http://arxiv.org/abs/2207.12644
Autor:
Singh, Rohan Pratap, Kumagai, Iori, Gabas, Antonio, Benallegue, Mehdi, Yoshiyasu, Yusuke, Kanehiro, Fumio
Publikováno v:
2020 IEEE/SICE International Symposium on System Integration (SII)
In many robotic applications, the environment setting in which the 6-DoF pose estimation of a known, rigid object and its subsequent grasping is to be performed, remains nearly unchanging and might even be known to the robot in advance. In this paper
Externí odkaz:
http://arxiv.org/abs/2207.13264
Publikováno v:
2021 IEEE International Conference on Robotics and Automation (ICRA)
Deep Convolutional Neural Networks (CNNs) have been successfully deployed on robots for 6-DoF object pose estimation through visual perception. However, obtaining labeled data on a scale required for the supervised training of CNNs is a difficult tas
Externí odkaz:
http://arxiv.org/abs/2011.03790
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