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20232026
most citedRelation DETR: Exploring Explicit Position Relation Prior for Object Detection

4 citations · 10 across the 20 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.RO2025

Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives

Shuanghao Bai, Wenxuan Song, Jiayi Chen +14

Recent advances in vision, language, and multimodal learning have significantly accelerated progress in robotic foundation models, with robotic manipulation remaining one of the mo…

cs.RO2025

VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation

Haoran Zhang, Shuanghao Bai, Wanqi Zhou +6

Robotic grasping is one of the most fundamental tasks in robotic manipulation, and grasp detection/generation has long been the subject of extensive research. Recently, language-dr…

cs.RO2025

Towards a Unified Understanding of Robot Manipulation: A Comprehensive Survey

Shuanghao Bai, Wenxuan Song, Jiayi Chen +15

Embodied intelligence has witnessed remarkable progress in recent years, driven by advances in computer vision, natural language processing, and the rise of large-scale multimodal…

cs.RO2025★ 1 cited

Long-VLA: Unleashing Long-Horizon Capability of Vision Language Action Model for Robot Manipulation

Yiguo Fan, Pengxiang Ding, Shuanghao Bai +10

Vision-Language-Action (VLA) models have become a cornerstone in robotic policy learning, leveraging large-scale multimodal data for robust and scalable control. However, existing…

cs.CV2025

Dual-Path Stable Soft Prompt Generation for Domain Generalization

Yuedi Zhang, Shuanghao Bai, Wanqi Zhou +2

Domain generalization (DG) aims to learn a model using data from one or multiple related but distinct source domains that can generalize well to unseen out-of-distribution target d…

cs.RO2025

Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation

Shuanghao Bai, Wanqi Zhou, Pengxiang Ding +3

Behavior Cloning (BC) is a widely adopted visual imitation learning method in robot manipulation. Current BC approaches often enhance generalization by leveraging large datasets an…