13 papers
FlashBack: Knowing When to Remember in Streaming Vision-Language Models
Yi Chen, MingMing Yu, Rui-Qi Wang +5
Streaming vision-language models must process continuously growing video streams under a bounded compute budget, creating a persistent tension between real-time perception and long…
GigaBrain-0.7: Scaling Embodied Foundation Models to Emergent Capabilities with a Three-System Architecture
GigaBrain Team, Angen Ye, Axiang Sun +56
Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task completion in structured sett…
AtlasVLA: Persistent World-Ego State Modeling for Vision-Language-Action Models
Guiyu Zhao, Longteng Guo, Yanghong Mei +7
While Vision-Language-Action (VLA) models have advanced embodied AI, their fundamentally reactive paradigm severely limits performance in partially observable and long-horizon task…
Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models
Haodong Yan, Junfeng Li, Junjie He +12
Mainstream World-Action Models (WAMs) adapt pretrained video generation models (VGMs) for robot control, transferring their learned dynamics prior for action prediction. These VGMs…
GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
GigaWorld Team, Angen Ye, Angyuan Ma +26
World Action Models (WAMs) improve robot policy learning by jointly modeling actions and future visual observations, using future scene evolution as dense supervision for physicall…
SurveilNav: Collaborative Object Goal Navigation with Robot and Surveillance System
Ming-Ming Yu, Qunbo Wang, Rongtao Xu +5
With the growing deployment of surveillance systems in factories, offices, and homes, integrating them with robots offers a promising direction for collaborative and efficient task…
NavWM: A Unified Navigation World Model for Foresight-Driven Planning
Yanghong Mei, Longteng Guo, Ming-Ming Yu +3
Conventional visual navigation policies often struggle with myopic decision-making and mode collapse in complex environments. While world models offer a promising alternative, exis…
GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning
GigaBrain Team, Boyuan Wang, Bohan Li +23
Vision-language-action (VLA) models that directly predict multi-step action chunks from current observations face inherent limitations due to constrained scene understanding and we…
EgoActor: Grounding Task Planning into Spatial-aware Egocentric Actions for Humanoid Robots via Visual-Language Models
Yu Bai, MingMing Yu, Chaojie Li +3
Deploying humanoid robots in real-world settings is fundamentally challenging, as it demands tight integration of perception, locomotion, and manipulation under partial-information…
UrbanNav: Learning Language-Guided Urban Navigation from Web-Scale Human Trajectories
Yanghong Mei, Yirong Yang, Longteng Guo +5
Navigating complex urban environments using natural language instructions poses significant challenges for embodied agents, including noisy language instructions, ambiguous spatial…
HiGS: Hierarchical Generative Scene Framework for Multi-Step Associative Semantic Spatial Composition
Jiacheng Hong, Kunzhen Wu, Mingrui Yu +4
Three-dimensional scene generation holds significant potential in gaming, film, and virtual reality. However, most existing methods adopt a single-step generation process, making i…
C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
Ming-Ming Yu, Fei Zhu, Wenzhuo Liu +4
Embodied agents are expected to perform object navigation in dynamic, open-world environments. However, existing approaches typically rely on static trajectories and a fixed set of…
3D-MoRe: Unified Modal-Contextual Reasoning for Embodied Question Answering
Rongtao Xu, Han Gao, Mingming Yu +6
With the growing need for diverse and scalable data in indoor scene tasks, such as question answering and dense captioning, we propose 3D-MoRe, a novel paradigm designed to generat…