1 citations · 1 across the 9 of their papers we have counts for
11 papers
META-Robot: Self-Cognition and Capability Scaling in Agentic Robots
Xuanzhi Feng, Zeyu Liu, Weiheng Gao +11
LLM-based robotic agent systems can generate programs for robot control, yet their generalization to new tasks often depends on elaborate scaffolding. Expanding their skill reperto…
World Action Learning via Interaction-Centric Spectral Latent Guidance
Zhiming Liu, Yikun Miao, Ying Chen +7
Learning general-purpose robot policies requires large-scale real-world interaction data, yet collecting robot demonstrations remains expensive and difficult to scale. Egocentric v…
OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling
Yikun Miao, Fangqi Zhu, Quanxin Shou +6
Generative world models aim to predict future states conditioned on actions, where action controllability is fundamental for reliable dynamics modeling. While recent efforts levera…
RedFlow: Redirect Failure into Action-level Corrections for Flow-matching VLA Policy
Zhengyang Yan, Junhao Li, Fangqi Zhu +6
Reinforcement learning (RL) can improve Vision-Language-Action (VLA) policies from deployment experience, but reward- and preference-based RL primarily identifies desirable behavio…
AdaTok: Self-Budgeting Image Tokenization with Quality-Preserving Dynamic Tokens
Xiaocheng Lu, Yuxi Chen, Jie Zhang +5
Image tokenizers, from 2D grids to recent 1D sequences, typically encode every image with the same fixed number of tokens. Yet visual complexity is highly heterogeneous, so a unifo…
Learning Spatiotemporal Sensitivity in Video LLMs via Counterfactual Reinforcement Learning
Dazhao Du, Jian Liu, Jialong Qin +7
Video large language models (Video LLMs) can achieve strong video-QA accuracy without reliably tracking spatiotemporal dynamics. A model may answer a motion question from static cu…
3D Generation for Embodied AI and Robotic Simulation: A Survey
Tianwei Ye, Yifan Mao, Minwen Liao +6
Embodied AI and robotic systems increasingly depend on scalable, diverse, and physically grounded 3D content for simulation-based training and real-world deployment. While 3D gener…
HALO: A Unified Vision-Language-Action Model for Embodied Multimodal Chain-of-Thought Reasoning
Quanxin Shou, Fangqi Zhu, Shawn Chen +9
Vision-Language-Action (VLA) models have shown strong performance in robotic manipulation, but often struggle in long-horizon or out-of-distribution scenarios due to the lack of ex…
WMPO: World Model-based Policy Optimization for Vision-Language-Action Models
Fangqi Zhu, Zhengyang Yan, Zicong Hong +3
Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, but their reliance on expert demonstrations limits their ability to learn…
Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning
Jian Liu, Jing Xu, Song Guo +10
Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-lev…
IRASim: A Fine-Grained World Model for Robot Manipulation
Fangqi Zhu, Hongtao Wu, Song Guo +3
World models allow autonomous agents to plan and explore by predicting the visual outcomes of different actions. However, for robot manipulation, it is challenging to accurately mo…