36 citations · 120 across the 10 of their papers we have counts for
13 papers
RoMo-GS: Efficient Online 3D Gaussian Splatting for Robot Mapping via Exposure-Integrated Motion Blur
Sangwoo Jung, Di Deng, Jiahui Fu +3
Online 3D Gaussian Splatting (3DGS) is emerging as a practical way to build a representation for robotics applications. However, deploying it on real-world platforms exposes multip…
Test-Time Spatial Reasoning for Robot Manipulation Using Generative Real-to-Sim
Ivan Kapelyukh, Yafei Hu, Ran Gong +6
Spatial reasoning is fundamental to general robot intelligence, as it enables robots to complete long-horizon tasks involving multi-object interaction. We introduce Simify, a train…
Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors
Zixing Wang, Kausik Sivakumar, Jinghuan Shang +5
Bridging the sim-to-real gap is a core challenge in deploying learned manipulation policies. Sim-to-real learning is attractive because it can replace expensive real robot demonstr…
EMPM: Embodied MPM for Modeling and Simulation of Deformable Objects
Yunuo Chen, Yafei Hu, Lingfeng Sun +3
Modeling deformable objects - especially continuum materials - in a way that is physically plausible, generalizable, and data-efficient remains challenging across 3D vision, graphi…
GaRLILEO: Gravity-aligned Radar-Leg-Inertial Enhanced Odometry
Chiyun Noh, Sangwoo Jung, Hanjun Kim +3
Deployment of legged robots for navigating challenging terrains (e.g., stairs, slopes, and unstructured environments) has gained increasing preference over wheel-based platforms. I…
NovaFlow: Zero-Shot Manipulation via Actionable Flow from Generated Videos
Hongyu Li, Lingfeng Sun, Yafei Hu +4
Enabling robots to execute novel manipulation tasks zero-shot is a central goal in robotics. Most existing methods assume in-distribution tasks or rely on fine-tuning with embodime…
Learning Generalizable Feature Fields for Mobile Manipulation
Ri-Zhao Qiu, Yafei Hu, Yuchen Song +8
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner so that robots can use both for navigation and manipulation. The latter requires c…
Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
Yafei Hu, Quanting Xie, Vidhi Jain +20
Building general-purpose robots that operate seamlessly in any environment, with any object, and utilizing various skills to complete diverse tasks has been a long-standing goal in…
Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments
Yafei Hu, Junyi Geng, Chen Wang +2
Autonomous exploration has many important applications. However, classic information gain-based or frontier-based exploration only relies on the robot current state to determine th…
Unsupervised Online Learning for Robotic Interestingness with Visual Memory
Chen Wang, Yuheng Qiu, Wenshan Wang +3
Autonomous robots frequently need to detect "interesting" scenes to decide on further exploration, or to decide which data to share for cooperation. These scenarios often require f…
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
Chen Wang, Wenshan Wang, Yuheng Qiu +2
In this paper, we explore the problem of interesting scene prediction for mobile robots. This area is currently underexplored but is crucial for many practical applications such as…
TartanAir: A Dataset to Push the Limits of Visual SLAM
Wenshan Wang, Delong Zhu, Xiangwei Wang +6
We present a challenging dataset, the TartanAir, for robot navigation tasks and more. The data is collected in photo-realistic simulation environments with the presence of moving o…
Instance-Aware Representation Learning and Association for Online Multi-Person Tracking
Hefeng Wu, Yafei Hu, Keze Wang +3
Multi-Person Tracking (MPT) is often addressed within the detection-to-association paradigm. In such approaches, human detections are first extracted in every frame and person traj…