4 citations · 7 across the 11 of their papers we have counts for
11 papers
Scaling Scientific Discovery Environments for Turn-Level Agentic RL
Yucheng Xu, Keyi Zhang, Yuyang Yu +4
Large language model agents have shown promising capabilities in data-driven scientific discovery tasks, where an agent interacts with an execution environment and produces a stati…
DemoBot: Efficient Learning of Bimanual Manipulation with Dexterous Hands From Third-Person Human Videos
Yucheng Xu, Xiaofeng Mao, Elle Miller +4
This work presents DemoBot, a learning framework that enables a dual-arm, multi-finger robotic system to acquire complex manipulation skills from a single unannotated RGB-D video d…
TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization
Jiayi Zhang, Yiming Zhang, Yuan Zheng +5
Urban traffic optimization is critical for improving transportation efficiency and alleviating congestion, particularly in large-scale dynamic networks. Traditional methods, such a…
Learning Long-Horizon Robot Manipulation Skills via Privileged Action
Xiaofeng Mao, Yucheng Xu, Zhaole Sun +3
Long-horizon contact-rich tasks are challenging to learn with reinforcement learning, due to ineffective exploration of high-dimensional state spaces with sparse rewards. The learn…
FACTS: A Factored State-Space Framework For World Modelling
Li Nanbo, Firas Laakom, Yucheng Xu +2
World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such…
3D Feature Distillation with Object-Centric Priors
Georgios Tziafas, Yucheng Xu, Zhibin Li +1
Grounding natural language to the physical world is a ubiquitous topic with a wide range of applications in computer vision and robotics. Recently, 2D vision-language models such a…
DHP-Mapping: A Dense Panoptic Mapping System with Hierarchical World Representation and Label Optimization Techniques
Tianshuai Hu, Jianhao Jiao, Yucheng Xu +3
Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric a…
Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter
Georgios Tziafas, Yucheng Xu, Arushi Goel +3
Robots operating in human-centric environments require the integration of visual grounding and grasping capabilities to effectively manipulate objects based on user instructions. T…
Learning Fine Pinch-Grasp Skills using Tactile Sensing from A Few Real-world Demonstrations
Xiaofeng Mao, Yucheng Xu, Ruoshi Wen +5
Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-effic…
Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE
Yucheng Xu, Li Nanbo, Arushi Goel +5
Videos depict the change of complex dynamical systems over time in the form of discrete image sequences. Generating controllable videos by learning the dynamical system is an impor…
Instance-wise Grasp Synthesis for Robotic Grasping
Yucheng Xu, Mohammadreza Kasaei, Hamidreza Kasaei +1
Generating high-quality instance-wise grasp configurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance fo…