1 citations · 2 across the 11 of their papers we have counts for
13 papers
Intern-S2-Preview: Scientific Agentic Foundation Model
Lei Bai, Jiaqi Cao, Chiyu Chen +121
Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sus…
Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
He Zhang, Lingzhu Xiang, Haitao Lin +23
In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pr…
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics
Open-H-Embodiment Consortium, :, Nigel Nelson +213
Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medic…
SlotVTG: Object-Centric Adapter for Generalizable Video Temporal Grounding
Jiwook Han, Geo Ahn, Youngrae Kim +1
Multimodal Large Language Models (MLLMs) have shown strong performance on Video Temporal Grounding (VTG). However, their coarse recognition capabilities are insufficient for fine-g…
Do Vision Language Models Understand Human Engagement in Games?
Ziyi Wang, Qizan Guo, Rishitosh Singh +1
Inferring human engagement from gameplay video is important for game design and player-experience research, yet it remains unclear whether vision--language models (VLMs) can infer…
UniMotion: A Unified Framework for Motion-Text-Vision Understanding and Generation
Ziyi Wang, Xinshun Wang, Shuang Chen +2
We present UniMotion, to our knowledge the first unified framework for simultaneous understanding and generation of human motion, natural language, and RGB images within a single a…
Universal Skeleton Understanding via Differentiable Rendering and MLLMs
Ziyi Wang, Peiming Li, Xinshun Wang +3
Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet cannot process structured, non-visual data such as human skeletons. Existing methods either c…
Superman: Unifying Skeleton and Vision for Human Motion Perception and Generation
Xinshun Wang, Peiming Li, Ziyi Wang +5
Human motion analysis tasks, such as temporal 3D pose estimation, motion prediction, and motion in-betweening, play an essential role in computer vision. However, current paradigms…
Eval-Actions: Fine-Grained Execution Quality Evaluation for Robotic Manipulation
Mengyuan Liu, Juyi Sheng, Peiming Li +4
Although Vision--Action (VA) and Vision--Language--Action (VLA) policies have advanced robotic manipulation, their evaluation remains dominated by binary success rates, which obscu…
UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
Peiming Li, Ziyi Wang, Yulin Yuan +4
Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous hum…
Recognizing Actions from Robotic View for Natural Human-Robot Interaction
Ziyi Wang, Peiming Li, Hong Liu +5
Natural Human-Robot Interaction (N-HRI) requires robots to recognize human actions at varying distances and states, regardless of whether the robot itself is in motion or stationar…
MP1: MeanFlow Tames Policy Learning in 1-step for Robotic Manipulation
Juyi Sheng, Ziyi Wang, Peiming Li +1
In robot manipulation, robot learning has become a prevailing approach. However, generative models within this field face a fundamental trade-off between the slow, iterative sampli…