Applying the learning mechanism of natural living beings to endow intelligent robots with
humanoid perception and decision-making wisdom becomes an important force to promote …

Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied
agents. The core concept—reusing prior knowledge to learn in and from novel situations—is …

3D LiDAR sensors can provide 3D point clouds of the environment, and are widely used in
automobile navigation; while 2D LiDAR sensors can only provide point cloud in a 2D …

Robotic insertion tasks are characterized by contact and friction mechanics, making them
challenging for conventional feedback control methods due to unmodeled physical effects …

Recent studies in recommender systems emphasize the importance of dealing with the cold-
start problem ie the modeling of new users or items in the recommendation system. Meta …

DeepRacer is a platform for end-to-end experimentation with RL and can be used to
systematically investigate the key challenges in develo** intelligent control systems …