6 papers · 1 filter
DRL-TH: Jointly Utilizing Temporal Graph Attention and Hierarchical Fusion for UGV Navigation in Crowded Environments
Ruitong Li, Lin Zhang, Yuenan Zhao +3
Deep reinforcement learning (DRL) methods have demonstrated potential for autonomous navigation and obstacle avoidance of unmanned ground vehicles (UGVs) in crowded environments. M…
StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion
Le Ma, Ziyu Meng, Tengyu Liu +4
Humanoid robots are anticipated to acquire a wide range of locomotion capabilities while ensuring natural movement across varying speeds and terrains. Existing methods encounter a…
Where to Fetch: Extracting Visual Scene Representation from Large Pre-Trained Models for Robotic Goal Navigation
Yu Li, Dayou Li, Chenkun Zhao +3
To complete a complex task where a robot navigates to a goal object and fetches it, the robot needs to have a good understanding of the instructions and the surrounding environment…
MPGNet: Learning Move-Push-Grasping Synergy for Target-Oriented Grasping in Occluded Scenes
Dayou Li, Chenkun Zhao, Shuo Yang +3
This paper focuses on target-oriented grasping in occluded scenes, where the target object is specified by a binary mask and the goal is to grasp the target object with as few robo…
Integrating Controllable Motion Skills from Demonstrations
Honghao Liao, Zhiheng Li, Ziyu Meng +3
The expanding applications of legged robots require their mastery of versatile motion skills. Correspondingly, researchers must address the challenge of integrating multiple divers…
VLMPC: Vision-Language Model Predictive Control for Robotic Manipulation
Wentao Zhao, Jiaming Chen, Ziyu Meng +3
Although Model Predictive Control (MPC) can effectively predict the future states of a system and thus is widely used in robotic manipulation tasks, it does not have the capability…