69 citations · 145 across the 15 of their papers we have counts for
13 papers · 1 filter
LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation
Shaoan Wang, Aocheng Luo, Fei Huang +17
Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodiments. Modern vision-language…
Light-Loco-Parkour: Versatile Perceptive Whole-Body Locomotion via Multi-Skill Distillation
Hongming Chen, Zhuoran Li, Hongxi Wang +9
Existing humanoid whole-body control systems still fall short of the way humans move through cluttered terrain: they either track expressive whole-body references without terrain g…
SMAT: Simultaneous and Self-Reinforced Mapping and Tracking in Dynamic Urban Scenariosorcing Framework for Simultaneous Mapping and Tracking in Unbounded Urban Environments
Tingxiang Fan, Bowen Shen, Yinqiang Zhang +5
Despite the increasing prevalence of robots in daily life, their navigation capabilities are still limited to environments with prior knowledge, such as a global map. To fully unlo…
DynamicFilter: an Online Dynamic Objects Removal Framework for Highly Dynamic Environments
Tingxiang Fan, Bowen Shen, Hua Chen +2
Emergence of massive dynamic objects will diversify spatial structures when robots navigate in urban environments. Therefore, the online removal of dynamic objects is critical. In…
DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation with Differentiable Simulator
Sirui Chen, Yunhao Liu, Jialong Li +3
Dynamic state representation learning is an important task in robot learning. Latent space that can capture dynamics related information has wide application in areas such as accel…
Crowd-Driven Mapping, Localization and Planning
Tingxiang Fan, Dawei Wang, Wenxi Liu +1
Navigation in dense crowds is a well-known open problem in robotics with many challenges in mapping, localization, and planning. Traditional solutions consider dense pedestrians as…
Autonomous Social Distancing in Urban Environments using a Quadruped Robot
Tingxiang Fan, Zhiming Chen, Xuan Zhao +5
COVID-19 pandemic has become a global challenge faced by people all over the world. Social distancing has been proved to be an effective practice to reduce the spread of COVID-19.…
Learning Resilient Behaviors for Navigation Under Uncertainty
Tingxiang Fan, Pinxin Long, Wenxi Liu +3
Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not…
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
Fan Wang, Bo Zhou, Ke Chen +5
Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety an…
Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds
Tingxiang Fan, Xinjing Cheng, Jia Pan +4
We aim to enable a mobile robot to navigate through environments with dense crowds, e.g., shopping malls, canteens, train stations, or airport terminals. In these challenging envir…
Safe Navigation with Human Instructions in Complex Scenes
Zhe Hu, Jia Pan, Tingxiang Fan +2
In this paper, we present a robotic navigation algorithm with natural language interfaces, which enables a robot to safely walk through a changing environment with moving persons b…
Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios
Tingxiang Fan, Pinxin Long, Wenxi Liu +1
In this paper, we present a decentralized sensor-level collision avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, o…
CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios
Tingxiang Fan, Xinjing Cheng, Jia Pan +2
Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training fram…