303 citations · 533 across the 92 of their papers we have counts for
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MoDex: Towards Universal Multi-Object Dexterous Grasping
Jing Xiong, Jiale Xie, Haoqi Yuan +2
Multi-object grasping is a fundamental yet challenging problem, constrained by data collection costs, varying spatial layouts, and the need for broad generalization. To address the…
PLAT: Sparse Timed Keyframe Motion Tracking for Humanoid Control via Privileged Latent Transition Learning
Zepeng Wang, Jiangxing Wang, Chao Ma +2
Humanoid motion tracking policies rely on dense frame-by-frame references, limiting their use as high-level motion controllers for planning and interactive motion generation. We st…
World Models for Embodied Intelligence: From Plausible to Controllable to Actionable
Nanjie Yao, Hao Wang, Chong Cheng +10
World models connect perception and decision-making in embodied intelligence by maintaining hidden state, anticipating consequences, comparing interventions, and adapting when exec…
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…
X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models
Boyu Li, Chaoyi Xu, Haoqi Yuan +5
Learning universal policies from cross-embodied data remains a fundamental challenge in robotics. Although Vision-Language-Action (VLA) models are pre-trained on large and diverse…
RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning
Chaoyi Xu, Yixuan Jiang, Jiahui Huan +7
Learning dexterous manipulation requires demonstrations that preserve fine hand-object interactions while remaining executable at deployment. Existing pipelines either lose deploya…
From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
Zhiyuan Feng, Qixiu Li, Huizhi Liang +12
Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most existing approaches rely on large…
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…
Unmasking the Illusion of Embodied Reasoning in Vision-Language-Action Models
Haiweng Xu, Sipeng Zheng, Hao Luo +3
Recent Vision-Language-Action (VLA) models report impressive success rates on standard robotic benchmarks, fueling optimism about general-purpose physical intelligence. However, re…
Conservative Offline Robot Policy Learning via Posterior-Transition Reweighting
Wanpeng Zhang, Hao Luo, Sipeng Zheng +6
Offline post-training adapts a pretrained robot policy to a target dataset by supervised regression on recorded actions. In practice, robot datasets are heterogeneous: they mix emb…
Joint-Aligned Latent Action: Towards Scalable VLA Pretraining in the Wild
Hao Luo, Ye Wang, Wanpeng Zhang +5
Despite progress, Vision-Language-Action models (VLAs) are limited by a scarcity of large-scale, diverse robot data. While human manipulation videos offer a rich alternative, exist…
General Humanoid Whole-Body Control via Pretraining and Fast Adaptation
Zepeng Wang, Jiangxing Wang, Shiqing Yao +8
Learning a general whole-body controller for humanoid robots remains challenging due to the diversity of motion distributions, the difficulty of fast adaptation, and the need for r…
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…
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…
UniTacHand: Unified Spatio-Tactile Representation for Human to Robotic Hand Skill Transfer
Chi Zhang, Penglin Cai, Haoqi Yuan +2
Tactile sensing is crucial for robotic hands to achieve human-level dexterous manipulation, especially in scenarios with visual occlusion. However, its application is often hindere…
Universal Dexterous Functional Grasping via Demonstration-Editing Reinforcement Learning
Chuan Mao, Haoqi Yuan, Ziye Huang +3
Reinforcement learning (RL) has achieved great success in dexterous grasping, significantly improving grasp performance and generalization from simulation to the real world. Howeve…
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…
SENTINEL: A Fully End-to-End Language-Action Model for Humanoid Whole Body Control
Yuxuan Wang, Haobin Jiang, Shiqing Yao +2
Existing humanoid control systems often rely on teleoperation or modular generation pipelines that separate language understanding from physical execution. However, the former is e…
Towards Proprioception-Aware Embodied Planning for Dual-Arm Humanoid Robots
Boyu Li, Siyuan He, Hang Xu +10
In recent years, Multimodal Large Language Models (MLLMs) have demonstrated the ability to serve as high-level planners, enabling robots to follow complex human instructions. Howev…
DemoGrasp: Universal Dexterous Grasping from a Single Demonstration
Haoqi Yuan, Ziye Huang, Ye Wang +3
Universal grasping with multi-fingered dexterous hands is a fundamental challenge in robotic manipulation. While recent approaches successfully learn closed-loop grasping policies…
JAEGER: Dual-Level Humanoid Whole-Body Controller
Ziluo Ding, Haobin Jiang, Yuxuan Wang +7
This paper presents JAEGER, a dual-level whole-body controller for humanoid robots that addresses the challenges of training a more robust and versatile policy. Unlike traditional…
RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control
Junpeng Yue, Zepeng Wang, Yuxuan Wang +7
This paper focuses on a critical challenge in robotics: translating text-driven human motions into executable actions for humanoid robots, enabling efficient and cost-effective lea…
DualTHOR: A Dual-Arm Humanoid Simulation Platform for Contingency-Aware Planning
Boyu Li, Siyuan He, Hang Xu +9
Developing embodied agents capable of performing complex interactive tasks in real-world scenarios remains a fundamental challenge in embodied AI. Although recent advances in simul…