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Yuhong Deng

10 accepted papers

2026

GSON: A Group-Based Social Navigation Framework with Large Multimodal Model

ICRA 2026poster

With the increasing presence of service robots and autonomous vehicles in human environments, navigation systems need to evolve beyond simple destination reach to incorporate social awareness. This paper introduces GSON, a novel group-based social navigation framework that leverages Large Multimodal…

2025

GSON: A Group-Based Social Navigation Framework With Large Multimodal Model

RA-L 2025

With the increasing presence of service robots and autonomous vehicles in human environments, navigation systems need to evolve beyond simple destination reach to incorporate social awareness. This paper introduces <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/199

Cited by 10SourceScholar
2025

Learning Generalizable Language-Conditioned Cloth Manipulation from Long Demonstrations

IROS 2025

Multi-step cloth manipulation is a challenging problem for robots due to the high-dimensional state spaces and the dynamics of cloth. Despite recent significant advances in end-to-end imitation learning for multi-step cloth manipulation skills, these methods fail to generalize to unseen tasks. Our i

Cited by 2SourceScholar
2025

MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence

CoRL 2025poster

Imitating tool manipulation from human videos offers an intuitive approach to teaching robots, while also providing a promising and scalable alternative to labor-intensive teleoperation data collection for visuomotor policy learning. While humans can mimic tool manipulation behavior by observing oth…

Cited by 0SourceScholar
2024

Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics

ICRA 2024poster

Multi-task learning of deformable object manipulation is a challenging problem in robot manipulation. Most previous works address this problem in a goal-conditioned way and adapt goal images to specify different tasks, which limits the multi-task learning performance and can not generalize to new ta…

Cited by 14SourceScholar
2023

Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention

RA-L 2023

Sequential multi-step cloth manipulation is a challenging problem in robotic manipulation, requiring a robot to perceive the cloth state and plan a sequence of chained actions leading to the desired state. Most previous works address this problem in a goal-conditioned way, and goal observation must

Cited by 33SourcecodeScholar
2022

Deep Reinforcement Learning Based on Local GNN for Goal-Conditioned Deformable Object Rearranging

IROS 2022poster

Object rearranging is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a goal configuration. Previous studies focus on designing an expert system for each specific task by model-based or data-driven approaches and the application scena…

Cited by 17SourceScholar
2019

Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

IROS 2019poster

In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and t…

Cited by 103SourceScholar