161 citations · 161 across the 12 of their papers we have counts for
14 papers
Organize Primitives into Semantic Parts: Reinforcement Reasoning for 3D Segmentation
Xiaoming Gong, Ruoyu Wu, Zhenhong Sun +5
Primitive-based 3D segmentation offers a compact and explicit alternative to dense surface prediction, naturally supporting structural abstraction and boundary localization. Howeve…
Hi-TOPS: Hierarchical Topology-aware Scoring Prior for 3D Part Decomposition
Ruoyu Wu, Zhenhong Sun, Xiaoming Gong +5
Accurate 3D part decomposition requires separating shapes into structurally meaningful components with precise boundaries while preserving articulation seams and thin attachments.…
Look-Before-Move: Narrative-Grounded World Visual Attention in Dynamic 3D Story Worlds
Jiaming Bian, Bingliang Li, Yuehao Wu +5
As embodied AI and world models increasingly operate in dynamic 3D environments, visual perception must move beyond passively interpreting given observations toward actively decidi…
ForceBand: Learning Forceful Manipulation with sEMG
Botao He, Zhi Wang, Linna Kuang +8
Human demonstrations are a scalable data source for learning robot manipulation policies. However, common sources of human demonstration data, such as motion-capture trajectories a…
Social Structure Matters in 3D Human-Human Interaction Generation
Zhongju Wang, Beier Wang, Yatao Bian +6
Although text-to-motion generation has achieved strong progress in synthesizing realistic single-person motions from language, extending it to text-driven 3D human-human interactio…
HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos
Zhi Wang, Botao He, Kelin Yu +4
Human egocentric video captures rich manipulation demonstrations without any robot hardware, yet transferring these skills to robots remains challenging due to the embodiment gap b…
DuetHOI: Language-Guided Bimanual Hand--Object Motion Generation with Articulation Planning and Contact Refinement
Zhi Wang, Yuyan Liu, Liu liu +2
Bimanual articulated-object interaction generation requires a model to capture the evolution of object articulation, coordination between the two hands, and fine-grained hand--obje…
Learning with Challenges: Adaptive Difficulty-Aware Data Generation for Mobile GUI Agent Training
Linjia Kang, Zhimin Wang, Yongkang Zhang +5
Large-scale, high-quality interaction trajectories are essential for advancing mobile Graphical User Interface (GUI) agents. While existing methods typically rely on labor-intensiv…
Grounding Large Language Models as Generalizable Policies in Network Control
Duo Wu, Linjia Kang, Zhimin Wang +9
Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure. Yet network cont…
IGen: Scalable Data Generation for Robot Learning from Open-World Images
Chenghao Gu, Haolan Kang, Junchao Lin +10
The rise of generalist robotic policies has created an exponential demand for large-scale training data. However, on-robot data collection is labor-intensive and often limited to s…
Collaborative Belief Reasoning with LLMs for Efficient Multi-Agent Collaboration
Zhimin Wang, Duo Wu, Shaokang He +6
Effective real-world multi-agent collaboration requires not only accurate planning but also the ability to reason about collaborators' intents--a crucial capability for avoiding mi…
DoorBot: Closed-Loop Task Planning and Manipulation for Door Opening in the Wild with Haptic Feedback
Zhi Wang, Yuchen Mo, Shengmiao Jin +1
Robots operating in unstructured environments face significant challenges when interacting with everyday objects like doors. They particularly struggle to generalize across diverse…
CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning
Duo Wu, Jinghe Wang, Yuan Meng +3
Utilizing large language models (LLMs) for tool planning has emerged as a promising avenue for developing general AI systems, where LLMs automatically schedule external tools (e.g.…
NetLLM: Adapting Large Language Models for Networking
Duo Wu, Xianda Wang, Yaqi Qiao +4
Many networking tasks now employ deep learning (DL) to solve complex prediction and optimization problems. However, current design philosophy of DL-based algorithms entails intensi…