17 citations · 19 across the 13 of their papers we have counts for
16 papers
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
Chi Zhang, Penglin Cai, Ziheng Xi +6
As an essential modality for dexterous and contact-rich tasks, tactile sensing provides precise force feedback that cannot be reliably inferred from vision. However, limited by har…
Being-H0.7: A Latent World-Action Model from Egocentric Videos
Hao Luo, Wanpeng Zhang, Yicheng Feng +6
Visual-Language-Action models (VLAs) have advanced generalist robot control by mapping multimodal observations and language instructions directly to actions, but sparse action supe…
Diverse Skill Discovery for Quadruped Robots via Unsupervised Learning
Ruopeng Cui, Yifei Bi, Haojie Luo +1
Reinforcement learning necessitates meticulous reward shaping by specialists to elicit target behaviors, while imitation learning relies on costly task-specific data. In contrast,…
Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularization
Ye Wang, Sipeng Zheng, Hao Luo +9
While Vision-Language-Action (VLA) models show strong promise for generalist robot control, it remains unclear whether -- and under what conditions -- the standard "scale data" rec…
Accelerated Mirror Descent Method through Variable and Operator Splitting
Long Chen, Hao Luo, Jingrong Wei +2
Mirror descent uses the mirror function to encode geometry and constraints, improving convergence while preserving feasibility. Accelerated Mirror Descent Methods (Acc-MD) are deri…
Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization
Hao Luo, Ye Wang, Wanpeng Zhang +9
We introduce Being-H0.5, a foundational Vision-Language-Action (VLA) model designed for robust cross-embodiment generalization across diverse robotic platforms. While existing VLAs…
Spatial-Aware VLA Pretraining through Visual-Physical Alignment from Human Videos
Yicheng Feng, Wanpeng Zhang, Ye Wang +4
Vision-Language-Action (VLA) models provide a promising paradigm for robot learning by integrating visual perception with language-guided policy learning. However, most existing ap…
Transport Discrepancy as a Reliability Signal for Vision-Language-Action Models
Wanpeng Zhang, Ye Wang, Hao Luo +6
Vision-language-action (VLA) models that generate continuous action chunks via flow matching lack an internal signal for judging whether a given prediction is reliable. Distributio…
Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds
Weihao Tan, Xiangyang Li, Yunhao Fang +11
We introduce Lumine, the first open recipe for developing generalist agents capable of completing hours-long complex missions in real time within challenging 3D open-world environm…
Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos
Hao Luo, Yicheng Feng, Wanpeng Zhang +7
We introduce Being-H0, a dexterous Vision-Language-Action model (VLA) trained on large-scale human videos. Existing VLAs struggle with complex manipulation tasks requiring high dex…
Unified Multimodal Understanding via Byte-Pair Visual Encoding
Wanpeng Zhang, Yicheng Feng, Hao Luo +4
Multimodal large language models (MLLMs) have made significant progress in vision-language understanding, yet effectively aligning different modalities remains a fundamental challe…
Intelligent logistics management robot path planning algorithm integrating transformer and GCN network
Hao Luo, Jianjun Wei, Shuchen Zhao +3
This research delves into advanced route optimization for robots in smart logistics, leveraging a fusion of Transformer architectures, Graph Neural Networks (GNNs), and Generative…
Pre-trained Visual Dynamics Representations for Efficient Policy Learning
Hao Luo, Bohan Zhou, Zongqing Lu
Pre-training for Reinforcement Learning (RL) with purely video data is a valuable yet challenging problem. Although in-the-wild videos are readily available and inhere a vast amoun…
Reinforcement Learning Friendly Vision-Language Model for Minecraft
Haobin Jiang, Junpeng Yue, Hao Luo +2
One of the essential missions in the AI research community is to build an autonomous embodied agent that can achieve high-level performance across a wide spectrum of tasks. However…
Model-Based Decentralized Policy Optimization
Hao Luo, Jiechuan Jiang, Zongqing Lu
Decentralized policy optimization has been commonly used in cooperative multi-agent tasks. However, since all agents are updating their policies simultaneously, from the perspectiv…
A Survey on Transformers in Reinforcement Learning
Wenzhe Li, Hao Luo, Zichuan Lin +3
Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings. Recently, a similar surge of using Transformers has appeared in…