7 citations · 14 across the 14 of their papers we have counts for
15 papers
GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization
Xiaosong Jia, Bowen Yang, Zuhao Ge +17
Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Existing VLAs rely on end-to-end…
: Resource-Aware Robust Manipulation via Taming Distributional Inconsistencies
Checheng Yu, Chonghao Sima, Gangcheng Jiang +14
High-reliability long-horizon robotic manipulation has traditionally relied on large-scale data and compute to understand complex real-world dynamics. However, we identify that the…
Seeing Farther and Smarter: Value-Guided Multi-Path Reflection for VLM Policy Optimization
Yanting Yang, Shenyuan Gao, Qingwen Bu +2
Solving complex, long-horizon robotic manipulation tasks requires a deep understanding of physical interactions, reasoning about their long-term consequences, and precise high-leve…
WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control
Haoran Jiang, Jin Chen, Qingwen Bu +8
Humanoid robots require precise locomotion and dexterous manipulation to perform challenging loco-manipulation tasks. Yet existing approaches, modular or end-to-end, are deficient…
Agility Meets Stability: Versatile Humanoid Control with Heterogeneous Data
Yixuan Pan, Ruoyi Qiao, Li Chen +8
Humanoid robots are envisioned to perform a wide range of tasks in human-centered environments, requiring controllers that combine agility with robust balance. Recent advances in l…
UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
Qingwen Bu, Yanting Yang, Jisong Cai +5
A generalist robot should perform effectively across various environments. However, most existing approaches heavily rely on scaling action-annotated data to enhance their capabili…
AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
AgiBot World Contributors, AgiBot-World-Contributors, Qingwen Bu +50
We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 millio…
Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation
Qingwen Bu, Jia Zeng, Li Chen +7
Despite significant progress in robotics and embodied AI in recent years, deploying robots for long-horizon tasks remains a great challenge. Majority of prior arts adhere to an ope…
Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation
Qingwen Bu, Hongyang Li, Li Chen +5
The increasing demand for versatile robotic systems to operate in diverse and dynamic environments has emphasized the importance of a generalist policy, which leverages a large cro…
Learning Manipulation by Predicting Interaction
Jia Zeng, Qingwen Bu, Bangjun Wang +12
Representation learning approaches for robotic manipulation have boomed in recent years. Due to the scarcity of in-domain robot data, prevailing methodologies tend to leverage larg…
Embodied Understanding of Driving Scenarios
Yunsong Zhou, Linyan Huang, Qingwen Bu +7
Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded…
Bias Testing and Mitigation in LLM-based Code Generation
Dong Huang, Jie M. Zhang, Qingwen Bu +3
As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as tho…
SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression
Qingwen Bu, Sungrae Park, Minsoo Khang +1
Existing techniques for text detection can be broadly classified into two primary groups: segmentation-based and regression-based methods. Segmentation models offer enhanced robust…
Towards Building More Robust Models with Frequency Bias
Qingwen Bu, Dong Huang, Heming Cui
The vulnerability of deep neural networks to adversarial samples has been a major impediment to their broad applications, despite their success in various fields. Recently, some wo…
Adversarial Feature Map Pruning for Backdoor
Dong Huang, Qingwen Bu
Deep neural networks have been widely used in many critical applications, such as autonomous vehicles and medical diagnosis. However, their security is threatened by backdoor attac…