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pro vyhledávání: '"Xiong, Ziyan"'
The advent of deep reinforcement learning (DRL) has significantly advanced the field of robotics, particularly in the control and coordination of quadruped robots. However, the complexity of real-world tasks often necessitates the deployment of multi
Externí odkaz:
http://arxiv.org/abs/2403.16015
Reinforcement Learning (RL) is notoriously data-inefficient, which makes training on a real robot difficult. While model-based RL algorithms (world models) improve data-efficiency to some extent, they still require hours or days of interaction to lea
Externí odkaz:
http://arxiv.org/abs/2310.16029
We propose a neural network-based approach for collision detection with deformable objects. Unlike previous approaches based on bounding volume hierarchies, our neural approach does not require an update of the spatial data structure when the object
Externí odkaz:
http://arxiv.org/abs/2202.02309