8 papers
Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Chassis Adaptation
Zhidong Wang, Jingsong Liang, Zirui Li +3
World model (WM)-based reinforcement learning enables sample-efficient end-to-end autonomous driving learning by imagining long-horizon trajectories in latent space. However, most…
ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation
Shizhe Zhang, Jingsong Liang, Zhitao Zhou +6
Existing methods for multi-agent navigation typically assume fully known environments, offering limited support for partially known scenarios with outdated or imperfect prior maps,…
Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning
Jiaqi Cheng, Mingfeng Fan, Xuefeng Zhang +4
Effective and efficient task planning is essential for mobile robots, especially in applications like warehouse retrieval and environmental monitoring. These tasks often involve se…
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…
IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity
Derek Ming Siang Tan, Yixiao Ma, Jingsong Liang +3
Information sharing is critical in time-sensitive and realistic multi-robot exploration, especially for smaller robotic teams in large-scale environments where connectivity may be…
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…