1 code implementation • 7 Mar 2023 • Rohan Pratap Singh, Zhaoming Xie, Pierre Gergondet, Fumio Kanehiro
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.
1 code implementation • 27 Jul 2022 • Rohan Pratap Singh, Iori Kumagai, Antonio Gabas, Mehdi Benallegue, Yusuke Yoshiyasu, Fumio Kanehiro
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.
1 code implementation • 26 Jul 2022 • Rohan Pratap Singh, Mehdi Benallegue, Mitsuharu Morisawa, Rafael Cisneros, Fumio Kanehiro
To enable the application of RL policies for humanoid robots in real-world settings, it is crucial to build a system that can achieve robust walking in any direction, on 2D and 3D terrains, and be controllable by a user-command.
1 code implementation • 7 Nov 2020 • Rohan Pratap Singh, Mehdi Benallegue, Yusuke Yoshiyasu, Fumio Kanehiro
The sparse representation leads to the development of a dense model and the pose labels for each image frame in the set of scenes.