4 citations · 4 across the 18 of their papers we have counts for
22 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…
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…
OpenT2M: No-frill Motion Generation with Open-source,Large-scale, High-quality Data
Bin Cao, Sipeng Zheng, Hao Luo +3
Text-to-motion (T2M) generation aims to create realistic human movements from text descriptions, with promising applications in animation and robotics. Despite recent progress, cur…
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…
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…
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…
Robust Motion Generation using Part-level Reliable Data from Videos
Boyuan Li, Sipeng Zheng, Bin Cao +2
Extracting human motion from large-scale web videos offers a scalable solution to the data scarcity issue in character animation. However, some human parts in many video frames can…
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…
Being-M0.5: A Real-Time Controllable Vision-Language-Motion Model
Bin Cao, Sipeng Zheng, Ye Wang +5
Human motion generation has emerged as a critical technology with transformative potential for real-world applications. However, existing vision-language-motion models (VLMMs) face…
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…
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…
Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning
Jiazheng Liu, Sipeng Zheng, Börje F. Karlsson +1
Multimodal large language models (MLLMs), built on large-scale pre-trained vision towers and language models, have shown great capabilities in multimodal understanding. However, mo…
VideoOrion: Tokenizing Object Dynamics in Videos
Yicheng Feng, Yijiang Li, Wanpeng Zhang +4
We present VideoOrion, a Video Large Language Model (Video-LLM) that explicitly captures the key semantic information in videos - the spatial-temporal dynamics of objects throughou…
Scaling Large Motion Models with Million-Level Human Motions
Ye Wang, Sipeng Zheng, Bin Cao +4
Inspired by the recent success of LLMs, the field of human motion understanding has increasingly shifted toward developing large motion models. Despite some progress, current effor…
From Pixels to Tokens: Byte-Pair Encoding on Quantized Visual Modalities
Wanpeng Zhang, Zilong Xie, Yicheng Feng +4
Multimodal Large Language Models have made significant strides in integrating visual and textual information, yet they often struggle with effectively aligning these modalities. We…
UniCode: Learning a Unified Codebook for Multimodal Large Language Models
Sipeng Zheng, Bohan Zhou, Yicheng Feng +2
In this paper, we propose \textbf{UniCode}, a novel approach within the domain of multimodal large language models (MLLMs) that learns a unified codebook to efficiently tokenize vi…
LLaMA Rider: Spurring Large Language Models to Explore the Open World
Yicheng Feng, Yuxuan Wang, Jiazheng Liu +2
Recently, various studies have leveraged Large Language Models (LLMs) to help decision-making and planning in environments, and try to align the LLMs' knowledge with the world cond…
Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds
Sipeng Zheng, Jiazheng Liu, Yicheng Feng +1
Recent studies have presented compelling evidence that large language models (LLMs) can equip embodied agents with the self-driven capability to interact with the world, which mark…