6 accepted papers
Utilizing neural networks to predict potential regions containing optimal paths in advance and subsequently biasing the sampling probability towards these promising regions has been proven to effectively enhance the path planning efficiency of sampling-based algorithms. %In complex scenarios, unifor…
Motion generation in dynamic environments is crucial for human-machine interaction with redundant manipulators. In this context, we propose the Enhancing Safety and Manipulability (ESM) scheme, which integrates geometry-based dynamic obstacle avoidance, manipulability optimization,trajectory trackin…
Dexterous grasping is a crucial technique in humanoid robot manipulation. However, existing methods still fall short in effectively detecting dexterous grasps in cluttered environments. In this work, we propose DexMGNet, a novel multi-mode dexterous grasping framework designed for such challenging s
Motion generation in dynamic environments is crucial for human-machine interaction with redundant manipulators. In this context, we propose the Enhancing Safety and Manipulability (ESM) scheme, which integrates geometry-based dynamic obstacle avoidance, manipulability optimization, trajectory tracki
Humanoid robots are engineered to navigate terrains akin to those encountered by humans, which necessitates human-like locomotion and perceptual abilities. Currently, the most reliable controllers for humanoid motion rely exclusively on proprioception, a reliance that becomes both dangerous and unre
We propose NEDS-SLAM, a dense semantic SLAM system based on 3D Gaussian representation, that enables robust 3D semantic mapping, accurate camera tracking, and high-quality rendering in real-time. In the system, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www