8 papers
DynaHMRC: Decentralized Heterogeneous Multi-Robot Collaboration for Dynamic Tasks with Large Language Models
Wenhao Yu, Yu'ang Xie, Yifan Duan +5
Large language models (LLMs) provide robots with richer task understanding and adaptability, making them promising for coordinating heterogeneous multi-robot systems in long-horizo…
GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference
Yu Han, Lehan Pan, Jie Peng +4
Sparse Mixture of Experts (SMoE) enables scalable parameter growth in large language models (LLMs) by selectively activating a subset of experts, and its large parameter count nece…
Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving
Shumin Wang, Zhuoran Yang, Lidian Wang +7
The significant achievements of pre-trained models leveraging large volumes of data in the field of NLP and 2D vision inspire us to explore the potential of extensive data pre-trai…
CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous Driving
Junrui Zhang, Chenjie Wang, Jie Peng +4
Imitation learning based planning tasks on the nuPlan dataset have gained great interest due to their potential to generate human-like driving behaviors. However, open-loop trainin…
MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models
Wenhao Yu, Jie Peng, Yueliang Ying +3
The integration of large language models (LLMs) with robotics has significantly advanced robots' abilities in perception, cognition, and task planning. The use of natural language…
LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance
Wenhao Yu, Jie Peng, Huanyu Yang +4
The conditional diffusion model has been demonstrated as an efficient tool for learning robot policies, owing to its advancement to accurately model the conditional distribution of…
Rotation Initialization and Stepwise Refinement for Universal LiDAR Calibration
Yifan Duan, Xinran Zhang, Guoliang You +8
Autonomous systems often employ multiple LiDARs to leverage the integrated advantages, enhancing perception and robustness. The most critical prerequisite under this setting is the…
PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
Wenhao Yu, Jie Peng, Quecheng Qiu +3
Robot navigation using deep reinforcement learning (DRL) has shown great potential in improving the performance of mobile robots. Nevertheless, most existing DRL-based navigation m…