activity
20222026
most citedTODE-Trans: Transparent Object Depth Estimation with Transformer

2 citations · 3 across the 7 of their papers we have counts for

collaborators
Showing cs.ROShow all

9 papers · 1 filter

cs.RO2026

Temporal Logic Guided Universal Task Representations for Reinforcement Learning

Hao Zhang, Zhangli Zhou, Zhen Kan

Task guided agents demonstrate strong performance in a wide range of complex tasks. However, most existing task representation algorithms are tailored to specific contexts and stru…

cs.RO2026

Kilohertz-Safe: A Scalable Framework for Constrained Dexterous Retargeting

Yinxiao Tian, Ziyi Yang, Zinan Zhao +1

Dexterous hand teleoperation requires motion re-targeting methods that simultaneously achieve high-frequency real-time performance and enforcement of heterogeneous kinematic and sa…

cs.RO2025

A Novel Task-Driven Diffusion-Based Policy with Affordance Learning for Generalizable Manipulation of Articulated Objects

Hao Zhang, Zhen Kan, Weiwei Shang +1

Despite recent advances in dexterous manipulations, the manipulation of articulated objects and generalization across different categories remain significant challenges. To address…

cs.RO20241 cited

Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic Manipulation

Hao Zhang, Hao Wang, Xiucai Huang +2

Reinforcement Learning (RL) based methods have been increasingly explored for robot learning. However, RL based methods often suffer from low sampling efficiency in the exploration…

cs.RO2023

Lightweight Neural Path Planning

Jinsong Li, Shaochen Wang, Ziyang Chen +2

Learning-based path planning is becoming a promising robot navigation methodology due to its adaptability to various environments. However, the expensive computing and storage asso…

cs.RO2023

Vision-Based Reactive Planning and Control of Quadruped Robots in Unstructured Dynamic Environments

Tangyu Qian, Zhangli Zhou, Shaocheng Wang +3

Quadruped robots have received increasing attention for the past few years. However, existing works primarily focus on static environments or assume the robot has full observations…