9 citations · 9 across the 12 of their papers we have counts for
14 papers
FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation
Tianhao Zang, Shanze Wang, Ziqian Wang +4
Moving obstacles can block a previously clear route while a quadruped robot executes a motion command. We investigate whether predicting changing clearance improves navigation when…
GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces
Tianhao Zang, Zihan Liu, Shanze Wang +4
In this paper, we present a Goal-Aware Uncertainty-Guided Exploration (GAUGE) framework for planner-conditioned active calibration of opaque quadruped velocity interfaces. Commerci…
DCLP++: Learning to Navigate with Footprint Clearance and Relative Motion
Shanze Wang, Wei Zhang
We present DCLP++, a local navigation frameworkthat uses footprint clearance as the geometric basis for studying relative motion features in dynamic environments. Each valid LiDAR…
FlashNav: Training Deployable Robot Navigation Policies in Seconds
Shanze Wang, Yiwei Qian, Xinming Zhang +8
Training Deep Reinforcement Learning (DRL) navigation policies for different robot configurations remains time-consuming. We present FlashNav, a GPU-based framework that trains rob…
AgniNav: Configuration-Driven Cross-Embodiment Local Planning for Robot Navigation
Tianhao Zang, Siwei Cheng, Haidong Huang +2
Monocular local navigation is attractive for lightweight robots, but existing vision-based policies often couple perception to a specific body, camera height, and footprint, making…
Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation
Shanze Wang, Xinming Zhang, Siwei Cheng +4
Should a single collision necessarily terminate an entire navigation episode? In most deep reinforcement learning (DRL) frameworks for robot navigation, this remains the standard p…
Learning from Demonstration with Failure Awareness for Safe Robot Navigation
Xianghui Wang, Siwei Cheng, Shanze Wang +3
Learning from demonstration is widely used for robot navigation, yet it suffers from a fundamental limitation: demonstrations consist predominantly of successful behaviors and prov…
FSUNav: A Cerebrum-Cerebellum Architecture for Fast, Safe, and Universal Zero-Shot Goal-Oriented Navigation
Mingao Tan, Yiyang Li, Shanze Wang +2
Current vision-language navigation methods face substantial bottlenecks regarding heterogeneous robot compatibility, real-time performance, and navigation safety. Furthermore, they…
CeRLP: A Cross-embodiment Robot Local Planning Framework for Visual Navigation
Haoyu Xi, Mingao Tan, Xinming Zhang +5
Visual navigation for cross-embodiment robots is challenging due to variations in robot and camera configurations, which can lead to the failure of navigation tasks. Previous appro…
FastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control
Jun Xue, Junze Wang, Shanze Wang +3
Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a fundamental challenge, as the ''curse of dimensionality'' induces severe explorat…
MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation
Shanze Wang, Mingao Tan, Zhibo Yang +4
Deep Reinforcement Learning (DRL) based navigation methods have demonstrated promising results for mobile robots, but suffer from limited action flexibility in confined spaces. Con…
Enhancing Deep Reinforcement Learning-based Robot Navigation Generalization through Scenario Augmentation
Shanze Wang, Mingao Tan, Zhibo Yang +4
This work focuses on enhancing the generalization performance of deep reinforcement learning-based robot navigation in unseen environments. We present a novel data augmentation app…
Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles
Mingao Tan, Shanze Wang, Biao Huang +4
Distance-based reward mechanisms in deep reinforcement learning (DRL) navigation systems suffer from critical safety limitations in dynamic environments, frequently resulting in co…
ControlMTR: Control-Guided Motion Transformer with Scene-Compliant Intention Points for Feasible Motion Prediction
Jiawei Sun, Chengran Yuan, Shuo Sun +7
The ability to accurately predict feasible multimodal future trajectories of surrounding traffic participants is crucial for behavior planning in autonomous vehicles. The Motion Tr…