activity
20232026
most citedMFair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method

3 citations · 9 across the 16 of their papers we have counts for

collaborators

19 papers

cs.RO2026

ActiveArena: Benchmarking and Understanding Active Perception in Robotic Manipulation

Yibo Li, Enshen Zhou, Rui Chen +7

Active perception and manipulation are crucial for robots to interact with complex scenes. Existing benchmarks struggle to evaluate how robots effectively acquire and maintain info…

cs.RO2026

MA-VLA: Multi-Arm Vision-Language-Action Model for Collaboration and Compositional Generalization

Zaibin Zhang, Junlan Xiao, Zhongbo Zhang +11

Multi-arm collaboration is becoming a core capability in embodied manipulation. Recent vision-language-action (VLA) models integrate perception, language, and control, but most rep…

cs.RO2026

PRM-as-a-Judge: A Dense Evaluation Paradigm for Fine-Grained Robotic Auditing

Yuheng Ji, Yuyang Liu, Huajie Tan +15

Current robotic evaluation is still largely dominated by binary success rates, which collapse rich execution processes into a single outcome and obscure critical qualities such as…

cs.CV2026

HomeGuard: VLM-based Embodied Safeguard for Identifying Contextual Risk in Household Task

Xiaoya Lu, Yijin Zhou, Zeren Chen +6

Vision-Language Models (VLMs) empower embodied agents to execute complex instructions, yet they remain vulnerable to contextual safety risks where benign commands become hazardous…

cs.RO2026

SaPaVe: Towards Active Perception and Manipulation in Vision-Language-Action Models for Robotics

Mengzhen Liu, Enshen Zhou, Cheng Chi +6

Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven active perception with robust, viewpoi…

cs.RO2026

RoboBrain 2.5: Depth in Sight, Time in Mind

Huajie Tan, Enshen Zhou, Zhiyu Li +32

We introduce RoboBrain 2.5, a next-generation embodied AI foundation model that advances general perception, spatial reasoning, and temporal modeling through extensive training on…

cs.CV2025

Towards Cross-View Point Correspondence in Vision-Language Models

Yipu Wang, Yuheng Ji, Yuyang Liu +10

Cross-view correspondence is a fundamental capability for spatial understanding and embodied AI. However, it is still far from being realized in Vision-Language Models (VLMs), espe…

cs.RO2025

Towards Spatial Trace with Reasoning in Vision-Language Models for Robotics

Enshen Zhou, Yibo Li, Jingkun An +12

Spatial tracing, as a fundamental embodied interaction ability for robots, is inherently challenging as it requires multi-step metric-grounded reasoning compounded with complex spa…

cs.AI2025★ 1 cited

Prune4Web: DOM Tree Pruning Programming for Web Agent

Jiayuan Zhang, Kaiquan Chen, Zhihao Lu +3

Web automation employs intelligent agents to execute high-level tasks by mimicking human interactions with web interfaces. Despite the capabilities of recent Large Language Model (…

cs.RO2025

RoboOS-NeXT: A Unified Memory-based Framework for Lifelong, Scalable, and Robust Multi-Robot Collaboration

Huajie Tan, Cheng Chi, Xiansheng Chen +21

The proliferation of collaborative robots across diverse tasks and embodiments presents a central challenge: achieving lifelong adaptability, scalable coordination, and robust sche…

cs.RO2025

TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics

Yi Han, Enshen Zhou, Shanyu Rong +6

Vision-Language Models (VLMs) have shown remarkable capabilities in spatial reasoning, yet they remain fundamentally limited to qualitative precision and lack the computational pre…

cs.CV2025

MathSticks: A Benchmark for Visual Symbolic Compositional Reasoning with Matchstick Puzzles

Yuheng Ji, Huajie Tan, Cheng Chi +8

We introduce \textsc{MathSticks}, a benchmark for Visual Symbolic Compositional Reasoning (VSCR), which unifies visual perception, symbolic manipulation, and arithmetic consistency…

cs.RO2025

RoboBrain 2.0 Technical Report

BAAI RoboBrain Team, Mingyu Cao, Huajie Tan +50

We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in…

cs.RO2025

RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics

Enshen Zhou, Jingkun An, Cheng Chi +8

Spatial referring is a fundamental capability of embodied robots to interact with the 3D physical world. However, even with the powerful pretrained vision language models (VLMs), r…

cs.RO2024

Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection

Enshen Zhou, Qi Su, Cheng Chi +5

Automatic detection and prevention of open-set failures are crucial in closed-loop robotic systems. Recent studies often struggle to simultaneously identify unexpected failures rea…

cs.CV2024★ 2 cited

WorldSimBench: Towards Video Generation Models as World Simulators

Yiran Qin, Zhelun Shi, Jiwen Yu +10

Recent advancements in predictive models have demonstrated exceptional capabilities in predicting the future state of objects and scenes. However, the lack of categorization based…

cs.CV2024★ 2 cited

MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control

Enshen Zhou, Yiran Qin, Zhenfei Yin +5

It is a long-lasting goal to design a generalist-embodied agent that can follow diverse instructions in human-like ways. However, existing approaches often fail to steadily follow…

cs.CV2023★ 1 cited

MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception

Yiran Qin, Enshen Zhou, Qichang Liu +5

It is a long-lasting goal to design an embodied system that can solve long-horizon open-world tasks in human-like ways. However, existing approaches usually struggle with compound…

cs.LG2023★ 3 cited

MFair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method

Yinghao Zhu, Jingkun An, Enshen Zhou +8

In the data-driven artificial intelligence paradigm, models heavily rely on large amounts of training data. However, factors like sampling distribution imbalance can lead to issues…