Abstract: Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within fixed free workspace, therefore struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this problem, we propose AINav, an adaptive … [Read more...] about Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
Propioception
Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
Abstract: Motion planning for autonomous vision-based car racing is a challenging task in robotics. Classical racing systems divide the task into numerous submodules, undermining computational efficiency and leading to error propagation. Previous studies have demonstrated impressive reinforcement learning (RL) results for end-to-end autonomous driving. However, RL exhibits … [Read more...] about Vision-Based Policy Learning for High-Speed Autonomous Racing: A Two-Phase Learning Paradigm
Terrain-Adaptive Locomotion Control for an Underwater Hexapod Robot: Sensing Leg–Terrain Interaction With Proprioceptive Sensors
An underwater hexapod robot, driven by six C-shaped legs and eight thrusters, has the potential to traverse diverse terrains with unknown deformable properties, which can lead to unknown leg–terrain interaction forces. However, it is hard to use exteroceptive sensors such as cameras and sonars to recognize these properties. Here we propose a method to perceive the interaction … [Read more...] about Terrain-Adaptive Locomotion Control for an Underwater Hexapod Robot: Sensing Leg–Terrain Interaction With Proprioceptive Sensors
