1 citations · 1 across the 4 of their papers we have counts for
6 papers
GPO: Growing Policy Optimization for Legged Robot Locomotion and Whole-Body Control
Shuhao Liao, Peizhuo Li, Xinrong Yang +7
Training reinforcement learning (RL) policies for legged robots remains challenging due to high-dimensional continuous actions, hardware constraints, and limited exploration. Exist…
FARE: Fast-Slow Agentic Robotic Exploration
Shuhao Liao, Xuxin Lv, Jeric Lew +6
This work advances autonomous robot exploration by integrating agent-level semantic reasoning with fast local control. We introduce FARE, a hierarchical autonomous exploration fram…
HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward
Yuhong Cao, Yizhuo Wang, Jingsong Liang +4
This work pushes the boundaries of learning-based methods in autonomous robot exploration in terms of environmental scale and exploration efficiency. We present HEADER, an attentio…
HELM: Human-Preferred Exploration with Language Models
Shuhao Liao, Xuxin Lv, Yuhong Cao +3
In autonomous exploration tasks, robots are required to explore and map unknown environments while efficiently planning in dynamic and uncertain conditions. Given the significant v…
SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding
Shuhao Liao, Weihang Xia, Yuhong Cao +4
The Multi-Agent Path Finding (MAPF) problem aims to determine the shortest and collision-free paths for multiple agents in a known, potentially obstacle-ridden environment. It is t…
Bench-CoE: a Framework for Collaboration of Experts from Benchmark
Yuanshuai Wang, Xingjian Zhang, Jinkun Zhao +5
Large Language Models (LLMs) are key technologies driving intelligent systems to handle multiple tasks. To meet the demands of various tasks, an increasing number of LLMs-driven ex…