15 citations · 55 across the 21 of their papers we have counts for
14 papers · 1 filter
Implicit Virtual Leader: Decentralized Vision-Only Relative Pose Estimation for Multi-Robot Formations
Shiyuan Yang, Zelin Wang, Zhijia Tao +8
Classical leader-follower formation control suffers from single points of failure and error propagation, and relies on absolute localization sensors that are ill-suited for GPS-den…
SurgWMBench: A Vision-Based Benchmark for World-Modeling Surgical Instrument Motion Planning
Huanrong Liu, Weiliang Huang, Bob Zhang +3
Reliable surgical planning requires models that move beyond recognizing the current surgical step or imitating expert demonstrations, and instead anticipate how instrument motion r…
FabriVLA: A Lightweight Vision-Language-Action Model with Conformal Action Chunk Uncertainty
Shiyuan Yang, Borong Zhang, Jizheng Zhang +5
Vision-Language-Action (VLA) models have become a leading paradigm for general purpose robotic manipulation, but their computational cost and limited uncertainty awareness hinder p…
120 Minutes and a Laptop: Minimalist Image-goal Navigation via Unsupervised Exploration and Offline RL
Xiaoming Liu, Borong Zhang, Qingbiao Li +1
The prevailing paradigm for image-goal visual navigation often assumes access to large-scale datasets, substantial pretraining, and significant computational resources. In this wor…
SignNav: Leveraging Signage for Semantic Visual Navigation in Large-Scale Indoor Environments
Jian Sun, Yuming Huang, He Li +6
Humans routinely leverage semantic hints provided by signage to navigate to destinations within novel Large-Scale Indoor (LSI) environments, such as hospitals and airport terminals…
SutureFormer: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space
Huanrong Liu, Chunlin Tian, Tongyu Jia +8
Predicting surgical needle trajectories from endoscopic video is critical for robot-assisted suturing, enabling anticipatory planning, real-time guidance, and safer motion executio…
TacGNN:Learning Tactile-based In-hand Manipulation with a Blind Robot
Linhan Yang, Bidan Huang, Qingbiao Li +4
In this paper, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-…
See What the Robot Can't See: Learning Cooperative Perception for Visual Navigation
Jan Blumenkamp, Qingbiao Li, Binyu Wang +2
We consider the problem of navigating a mobile robot towards a target in an unknown environment that is endowed with visual sensors, where neither the robot nor the sensors have ac…
A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies
Jan Blumenkamp, Steven Morad, Jennifer Gielis +2
GNNs are a paradigm-shifting neural architecture to facilitate the learning of complex multi-agent behaviors. Recent work has demonstrated remarkable performance in tasks such as f…
The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts
Amanda Prorok, Jan Blumenkamp, Qingbiao Li +3
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of le…
Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection
Lifeng Zhou, Vishnu D. Sharma, Qingbiao Li +4
The problem of decentralized multi-robot target tracking asks for jointly selecting actions, e.g., motion primitives, for the robots to maximize target tracking performance with lo…
Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning
Qingbiao Li, Weizhe Lin, Zhe Liu +1
The domains of transport and logistics are increasingly relying on autonomous mobile robots for the handling and distribution of passengers or resources. At large system scales, fi…
Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning
Binyu Wang, Zhe Liu, Qingbiao Li +1
Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoid…
Graph Neural Networks for Decentralized Multi-Robot Path Planning
Qingbiao Li, Fernando Gama, Alejandro Ribeiro +1
Effective communication is key to successful, decentralized, multi-robot path planning. Yet, it is far from obvious what information is crucial to the task at hand, and how and whe…